컴퓨터 프로그램 내에서 데이터를 정렬, 저장 및 처리할 수 있는 다양한 방법을 Java에서는 데이터 구조라고 합니다. 이러한 구조는 데이터를 효과적으로 처리하고 관리하기 위한 체계적인 방법을 제공하여 삽입, 삭제, 검색 및 순회와 같은 유용한 작업을 가능하게 합니다.
이 기사에서는 Java의 데이터 구조와 관련된 모든 것을 살펴보고 초보자가 쉽고 효과적으로 이해할 수 있도록 도와줍니다.
- 자바란 무엇입니까?
- Java의 데이터 구조란 무엇입니까?
- Java의 데이터 구조 유형
- Java 데이터 구조의 장점
- 데이터 구조의 분류
- Java FAQ의 데이터 구조
자바란 무엇입니까?
Java는 방대한 표준 라이브러리와 플랫폼의 자유로움으로 유명한 인기 있는 객체 지향 프로그래밍 언어입니다. 다양한 플랫폼에서 재컴파일 없이 실행되는 프로그램을 생성하기 위한 견고한 아키텍처를 제공합니다. 잘 알려진 Java 라이브러리에는 다양한 데이터 유형을 효율적으로 처리할 수 있는 다양한 기록 시스템이 있습니다.
Java의 데이터 구조란 무엇입니까?
데이터가 컴퓨터 프로그램의 메모리에 구성되고 저장되는 방식은 Java 레코드 구조에 밀접하게 의존합니다. 잘 알려진 Java 라이브러리에는 중요한 유형의 내장 통계 구조가 포함되어 있습니다. 프로그래머가 짧고 간단한 방법으로 데이터를 저장하고 정렬할 수 있는 기록 시스템에는 연결된 목록, 스택, 대기열 및 배열이 포함됩니다. 개발자는 데이터 액세스, 변경 및 관리를 위한 다양한 메커니즘을 제공하므로 삽입, 삭제, 검색 및 정렬과 같은 작업을 신속하게 수행할 수 있습니다. Java 프로그래머는 이러한 데이터 구조를 사용하여 메모리 사용을 줄이고 프로그램의 전반적인 효율성을 상당히 높일 수 있습니다.
Java의 데이터 구조 유형
아래 나열된 Java의 데이터 구조 목록
- 배열
- 배열목록
- 링크드리스트
- 스택
- 대기줄
- 해시맵
- 해시세트
- 트리세트
- 트리맵
- 그래프
- 나무
아래 다이어그램은 Java의 데이터 구조 유형을 매우 명확하게 설명합니다.
데이터 구조 유형의 추가 분류:
데이터 구조에는 두 가지 유형이 있습니다.
- 원시 데이터 구조
- 비원시적 데이터 구조
1) 원시 데이터 구조: 기본 데이터 유형이라고도 하는 이는 Java의 기본 내장 데이터 유형입니다. 여기에는 다음이 포함됩니다.
2) 비원시적 데이터 구조: 비원시적 레코드 구조는 더 복잡하며 기본 정보 정렬로 구성됩니다. 또한 다음과 같은 두 가지 종류로 분류될 수 있습니다.
Java 데이터 구조의 장점
1) 배열:
배열은 Java의 데이터 구조와 관련하여 기본적이고 자주 사용되는 데이터 구조입니다. 이는 동일한 유형의 구성 요소를 고정 크기 컬렉션으로 저장하는 방법을 제공합니다. 배열은 인덱스에 따라 요소에 빠르고 쉽게 액세스할 수 있기 때문에 데이터를 관리하고 구성하는 데 중요한 도구입니다.
장점:
단점:
기능:
구현:
파일 이름: ArrayExample.java
import java.util.*; public class ArrayExample { public static void main(String[] args) { int[] numbers={10,20,30,40,50}; // Initialize an array of integers System.out.println('Element at index 0:'+numbers[0]); System.out.println('Element at index 2:'+numbers[2]); System.out.println('Element at index 4:'+numbers[4]); int sum=0; for (int i=0;i<numbers.length;i++) { sum+="numbers[i];" } system.out.println('sum of array elements:'+sum); numbers[2]="35;" update an element in the system.out.println('updated at index 2:'+numbers[2]); system.out.println('elements array:'); for (int number:numbers) system.out.println(number); < pre> <p> <strong>Output:</strong> </p> <pre> Element at index 0:10 Element at index 2:30 Element at index 4:50 Sum of array elements:150 Updated element at index 2:35 Elements in the array: 10 20 35 40 50 </pre> <h3>2) ArrayList:</h3> <p>ArrayList in Java is a dynamic data structure that allows for the storage and manipulation of elements. It is part of the Java Collections Framework and is implemented using an array internally.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Dynamic Size:</td> Unlike arrays, ArrayLists can dynamically grow or shrink in size as elements are added or removed. It eliminates the need for manual resizing and allows for handling varying amounts of data conveniently. </tr><tr><td>Easy Element Manipulation:</td> ArrayLists offer methods to add, remove, and modify elements at any position within the list. Its flexibility simplifies common operations such as insertion, deletion, and updating, making element manipulation more efficient. </tr><tr><td>Random Access:</td> ArrayLists support random Access to elements using their index, enabling quick retrieval and modification of elements at specific positions within the list. It facilitates efficient element access and enhances overall performance. </tr><tr><td>Compatibility with Java Collection Framework:</td> ArrayLists implement the List interface, making them compatible with other Collection classes in the Java Collections Framework. Its compatibility allows for seamless integration with various algorithms and operations provided by the framework. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Higher Memory Overhead:</td> ArrayLists require additional memory to maintain their internal structure, resulting in higher memory overhead compared to arrays. It can be a concern when dealing with large collections of elements. </tr><tr><td>Slower Insertion and Deletion:</td> Inserting or deleting elements in the middle of an ArrayList requires shifting elements, which can be time-consuming for large lists. In scenarios where frequent insertion or deletion operations are expected, other data structures like LinkedList may offer better performance. </tr><tr><td>Limited Performance for Search:</td> Searching for an element in an unsorted ArrayList requires iterating over the elements until a match is found. It is a linear search approach that results in slower search performance compared to data structures optimized for searching, such as HashSet or TreeMap. </tr><tr><td>No Primitive Type Support:</td> ArrayLists can only store objects and do not directly support primitive data types like int or char. To store primitive types, wrapper classes like Integer or Character need to be used, leading to potential autoboxing and unboxing overhead. </tr></ol> <p> <strong>Functions:</strong> </p> <ol class="points"> <tr><td>Creating an ArrayList:</td> Declare and initialize an ArrayList using the ArrayList class and specify the element type within the angle brackets. </tr><tr><td>Adding Elements:</td> Use the add method to append elements at the end of the ArrayList. </tr><tr><td>Accessing Elements:</td> Use the get technique to retrieve the price of detail at a selected index. </tr><tr><td>Modifying Elements:</td> Update the cost of detail at a specific index for the usage of the set approach. </tr><tr><td>Finding Size:</td> Use the dimensions method to get the cutting-edge quantity of factors in the ArrayList. </tr><tr><td>Removing Elements:</td> Use the remove approach to delete a detail at a specific index or via providing the object reference. </tr><tr><td>Iterating through the ArrayList:</td> Use loops to iterate over each element in the ArrayList and perform operations on them. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> ArrayListExample.java</p> <pre> import java.util.*; public class ArrayListExample { public static void main(String[] args) { // Create an ArrayList to store integers ArrayList numbers=new ArrayList(List.of(10,20,30,40,50)); //Access and print elements from the ArrayList System.out.println('Element at index 0:'+numbers.get(0)); System.out.println('Element at index 2:'+numbers.get(2)); System.out.println('Element at index 4:'+numbers.get(4)); // Calculate and print the sum of all elements in the ArrayList int sum=numbers.stream().mapToInt(Integer::intValue).sum(); System.out.println('Sum of ArrayList elements:'+sum); // Update an element in the ArrayList numbers.set(2,35); System.out.println('Updated element at index 2:'+numbers.get(2)); // Iterate through the ArrayList using a for-each loop and print the elements System.out.println('Elements in the ArrayList:'); for (int number:numbers) { System.out.println(number); } } } </pre> <p> <strong>Output:</strong> </p> <pre> Element at index 0:10 Element at index 2:30 Element at index 4:50 Sum of ArrayList elements:150 Updated element at index 2:35 Elements in the ArrayList: 10 20 35 40 50 </pre> <h3>3) Linked List:</h3> <p>A linked list is a linear data structure in which elements are stored in separate objects called nodes. A reference link to the following node in the sequence is included in each node's data element. The list's final node links to null, indicating that the list has ended.</p> <p>Unlike arrays, linked lists do not require contiguous memory allocation. Each node in a linked list can be allocated independently, allowing for dynamic memory allocation and efficient insertion and deletion operations.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Dynamic Size:</td> LinkedList can grow or shrink dynamically, making it suitable for varying or unknown data sizes. </tr><tr><td>Efficient Insertion and Deletion:</td> Inserting or deleting elements within a LinkedList is efficient, as it does not require shifting elements. </tr><tr><td>No Contiguous Memory Requirement:</td> LinkedList does not need contiguous memory allocation, making it flexible and suitable for unpredictable memory situations. </tr><tr><td>Easy Modification:</td> LinkedList allows easy modification of elements by changing reference pointers, enabling efficient manipulation. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Slower Random Access:</td> LinkedList has slower random Access as it requires traversing the list to access elements by index. </tr><tr><td>Increased Memory Overhead:</td> LinkedList requires additional memory for references and nodes, increasing memory overhead compared to arrays. </tr><tr><td>Inefficient Search:</td> LinkedList has slower search operations, requiring sequential iteration to find specific elements. </tr></ol> <p> <strong>Functions:</strong> </p> <ol class="points"> <tr><td>Creating a LinkedList:</td> Declare and initialize a LinkedList using the LinkedList class. </tr><tr><td>Adding Elements:</td> Use the add method to append elements at the end of the LinkedList. </tr><tr><td>Accessing Elements:</td> Use the get method to retrieve the value of an element at a specific index. </tr><tr><td>Modifying Elements:</td> Update the value of an element at a particular index using the set method. </tr><tr><td>Removing Elements:</td> Use the remove method to delete an element at a specific index or by providing the object reference. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> LinkedList1.java</p> <pre> import java.util.*; public class LinkedList1 { public static void main(String[] args) { // Create a LinkedList to store integers LinkedList linkedList1 = new LinkedList(); // Add elements to the LinkedList linkedList1.add(10); linkedList1.add(20); linkedList1.add(30); linkedList1.add(40); linkedList1.add(50); // Print the LinkedList System.out.println('LinkedList:'+linkedList1); // Remove an element from the LinkedList linkedList1.removeFirst(); System.out.println('LinkedList after removing first element:'+linkedList1); // Check if an element exists in the LinkedList boolean containsElement=linkedList1.contains(30); System.out.println('LinkedList contains element 30?'+containsElement); // Get the first and last elements of the LinkedList int firstElement=linkedList1.getFirst(); int lastElement=linkedList1.getLast(); System.out.println('First element:'+firstElement); System.out.println('Last element:'+lastElement); // Clear the LinkedList linkedList1.clear(); System.out.println('LinkedList after clearing:'+linkedList1); } } </pre> <p> <strong>Output:</strong> </p> <pre> LinkedList:[10, 20, 30, 40, 50] LinkedList after removing first element:[20, 30, 40, 50] LinkedList contains element 30?true First element:20 Last element:50 LinkedList after clearing:[] </pre> <h3>4) Stack:</h3> <p>The Last-In-First-Out (LIFO) principle dictates that the element that was most recently inserted is also the element that is removed first. A stack is a linear data structure that follows this rule. It employs the commands 'push' and 'pop' to add elements to the stack and, accordingly, remove the top element from the stack. The 'peek' technique additionally enables Access to the top element without removing it.</p> <p> <strong>Features of a stack:</strong> </p> <ol class="points"> <tr><td>LIFO behavior:</td> The last element pushed onto the stack is the first one to be popped out, making it suitable for applications where the order of insertion and removal is important. </tr><tr><td>Limited Access:</td> Stacks typically provide restricted Access to elements. You can only access the topmost element, and to reach other elements, you need to pop the elements above them. </tr><tr><td>Dynamic size:</td> Stacks can be implemented using arrays or linked lists, allowing for a dynamic size. They can grow or shrink as needed during runtime. </tr></ol> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Simplicity:</td> Stacks are easy to understand and implement. </tr><tr><td>Efficiency:</td> Insertion and deletion operations have a time complexity of O(1). </tr><tr><td>Function call management:</td> Stacks efficiently manage function calls and variable storage. </tr><tr><td>Undo/Redo functionality:</td> Stacks enable undo and redo operations in applications. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Limited Access:</td> Access to elements is restricted to the top of the stack. </tr><tr><td>Size restrictions:</td> Stacks may have size limitations depending on the implementation. </tr><tr><td>Not suitable for all scenarios:</td> Stacks are specific to LIFO behavior and may not be appropriate in other cases. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> StackExample.java</p> <pre> import java.util.Stack; public class StackExample { public static void main(String[] args) { // Create a stack Stack stack=new Stack(); // Push elements onto the stack stack.push(10); stack.push(20); stack.push(30); // Print the top element of the stack System.out.println('Top element:'+stack.peek()); // Pop elements from the stack int poppedElement=stack.pop(); System.out.println('Popped element:'+poppedElement); // Check if the stack is empty System.out.println('Is stack empty?'+stack.isEmpty()); // Get the size of the stack System.out.println('Stack size:'+stack.size()); // Iterate over the stack System.out.println('Stack elements:'); for (Integer element:stack) { System.out.println(element); } } } </pre> <p> <strong>Output:</strong> </p> <pre> Top element:30 Popped element:30 Is stack empty?false Stack size:2 Stack elements: 10 20 </pre> <h3>5) Queue:</h3> <p>A queue is a linear data structure in Java that follows the First-In-First-Out (FIFO) principle. It represents a collection of elements where elements are inserted at the rear and removed from the front.</p> <p> <strong>Features:</strong> </p> <ol class="points"> <tr><td>Enqueue:</td> Adding an element to the rear of the queue. </tr><tr><td>Dequeue:</td> Removing an element from the front of the queue. </tr><tr><td>Peek:</td> Retrieve the element at the front of the queue without removing it. </tr><tr><td>Size:</td> Determining the number of elements in the queue. </tr><tr><td>Empty Check:</td> Checking if the queue is empty. </tr></ol> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>FIFO Behavior:</td> Elements are processed in the order of their insertion, ensuring the preservation of the original sequence. </tr><tr><td>Efficient Insertion and Removal:</td> Adding and removing elements from a queue is fast and has a constant time complexity of O(1). </tr><tr><td>Synchronization:</td> Java provides synchronized queue implementations, making them safe for concurrent programming. </tr><tr><td>Standardized Interface:</td> The Queue interface in Java offers a common set of methods, allowing easy interchangeability between different queue implementations. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>No Random Access:</td> Queues do not support direct Access to elements in the middle. Accessing specific positions requires dequeuing preceding elements. </tr><tr><td>Limited Size:</td> Some queue implementations have a fixed size or capacity, leading to overflow or exceptions when exceeding the maximum size. </tr><tr><td>Inefficient Search:</td> Searching for an element in a queue requires dequeuing until a match is found, resulting in a linear search with potentially high time complexity. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> QueueExample.java</p> <pre> import java.util.Stack; import java.util.LinkedList; import java.util.Queue; public class QueueExample { public static void main(String[] args) { // Create a Queue to store integers Queue queue=new LinkedList(); // Enqueue elements to the Queue queue.offer(10); queue.offer(20); queue.offer(30); queue.offer(40); queue.offer(50); //Access and print the front element of the Queue System.out.println('Front element:'+queue.peek()); // Dequeue elements from the Queue and print them while (!queue.isEmpty()) { int element=queue.poll(); System.out.println('Dequeued element:'+element); } } } </pre> <p> <strong>Output:</strong> </p> <pre> Front element:10 Dequeued element:10 Dequeued element:20 Dequeued element:30 Dequeued element:40 Dequeued element:50 </pre> <h3>6) HashMap:</h3> <p>A HashMap is a data structure in Java that provides a way to store and retrieve key-value pairs. It is part of the Java Collections Framework and is implemented based on the hash table data structure.</p> <p> <strong>Functions:</strong> </p> <ul> <tr><td>put(key, value):</td> Inserts the specified key-value pair into the HashMap. </tr><tr><td>get(key):</td> Retrieves the value associated with the specified key. </tr><tr><td>containsKey(key):</td> Checks if the HashMap contains the specified key. </tr><tr><td>containsValue(value):</td> Checks if the HashMap contains the specified value. </tr><tr><td>remove(key):</td> Removes the key-value pair associated with the specified key from the HashMap. </tr><tr><td>size():</td> Returns the number of key-value pairs in the HashMap. </tr><tr><td>isEmpty():</td> Checks if the HashMap is empty. </tr><tr><td>keySet():</td> Returns a Set containing all the keys in the HashMap. </tr><tr><td>values():</td> Returns a Collection containing all the values in the HashMap. </tr><tr><td>clear():</td> Removes all the key-value pairs from the HashMap. </tr></ul> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Efficient Retrieval:</td> HashMap provides fast retrieval of values based on keys with constant-time complexity O(1). </tr><tr><td>Flexible Key-Value Pairing:</td> HashMap allows any non-null object as a key, enabling custom-defined keys for storing and retrieving data. </tr><tr><td>Dynamic Size:</td> HashMap can dynamically grow or shrink in size to handle varying amounts of data. </tr><tr><td>Compatibility with Java Collections Framework:</td> HashMap implements the Map interface, allowing seamless integration with other Collection classes. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Lack of Ordering:</td> HashMap does not preserve the order of elements. Use LinkedHashMap or TreeMap for specific ordering requirements. </tr><tr><td>Increased Memory Overhead:</td> HashMap requires additional memory for hash codes and internal structure compared to simpler data structures. </tr><tr><td>Slower Iteration:</td> Iterating over a HashMap can be slower compared to arrays or lists due to traversing the underlying hash table. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> HashMapExample.java</p> <pre> import java.util.HashMap; public class HashMapExample { public static void main(String[] args) { // Create a HashMap to store String keys and Integer values HashMap hashMap=new HashMap(); // Add key-value pairs to the HashMap hashMap.put('John',25); hashMap.put('Alice',30); hashMap.put('Bob',35); //Access and print values based on keys System.out.println('Age of John:'+hashMap.get('John')); System.out.println('Age of Alice:'+hashMap.get('Alice')); // Check if a key exists in the HashMap System.out.println('Is Bob present?'+hashMap.containsKey('Bob')); // Update the value associated with a key hashMap.put('Alice',32); // Remove a key-value pair from the HashMap hashMap.remove('John'); // Print all key-value pairs in the HashMap System.out.println('Key-Value pairs in the HashMap:'); for (String key : hashMap.keySet()) { System.out.println(key+':'+hashMap.get(key)); } // Check the size of the HashMap System.out.println('Size of the HashMap:'+hashMap.size()); } } </pre> <p> <strong>Output:</strong> </p> <pre> Age of John:25 Age of Alice:30 Is Bob present?true Key-Value pairs in the HashMap: Bob:35 Alice:32 Size of the HashMap:2 </pre> <h3>7) HashSet:</h3> <p>HashSet is a data structure in Java that implements the Set interface and stores elements in a hash table.</p> <p> <strong>Features:</strong> </p> <ol class="points"> <tr><td>Stores unique elements:</td> HashSet does not allow duplicate elements. Each element in the HashSet is unique. </tr><tr><td>Uses hash-based lookup:</td> HashSet uses the hash value of each element to determine its storage location, providing efficient element retrieval. </tr><tr><td>Unordered collection:</td> The elements in a HashSet are not stored in a specific order. The order of elements may change over time. </tr></ol> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Fast element lookup:</td> HashSet provides fast lookup operations, making it efficient to check if an element exists in the set. </tr><tr><td>No duplicate elements:</td> HashSet automatically handles duplicate elements and ensures that each element is unique. </tr><tr><td>Integration with Java Collections Framework:</td> HashSet implements the Set interface, making it compatible with other collection classes in the Java Collections Framework. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>No guaranteed order:</td> HashSet does not maintain the order of elements. If the order of elements is important, HashSet is not suitable. </tr><tr><td>No indexing:</td> HashSet does not provide direct indexing or positional Access to elements. To access elements, you need to iterate over the set. </tr><tr><td>Higher memory overhead:</td> HashSet requires additional memory to store hash values and maintain the hash table structure, resulting in higher memory usage compared to some other data structures. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> HashSetExample.java</p> <pre> import java.util.HashSet; public class HashSetExample { public static void main(String[] args) { // Create a HashSet HashSet set=new HashSet(); // Add elements to the HashSet set.add('Apple'); set.add('Banana'); set.add('Orange'); set.add('Grapes'); set.add('Mango'); // Print the HashSet System.out.println('HashSet:'+set); // Check if an element exists System.out.println('Contains 'Apple':'+set.contains('Apple')); // Remove an element set.remove('Banana'); // Print the updated HashSet System.out.println('Updated HashSet:'+set); // Get the size of the HashSet System.out.println('Size of HashSet:'+set.size()); // Clear the HashSet set.clear(); // Check if the HashSet is empty System.out.println('Is HashSet empty?'+set.isEmpty()); } } </pre> <p> <strong>Output:</strong> </p> <pre> HashSet:[Apple, Grapes, Mango, Orange, Banana] Contains 'Apple':true Updated HashSet:[Apple, Grapes, Mango, Orange] Size of HashSet:4 Is HashSet empty?true </pre> <h3>8) TreeSet:</h3> <p>TreeSet is an implementation of the SortedSet interface in Java that uses a self-balancing binary search tree called a red-black tree to store elements in sorted order.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Sorted Order:</td> TreeSet automatically maintains the elements in a sorted order based on their natural ordering or a custom comparator. It allows for efficient searching and retrieval of elements in ascending or descending order. </tr><tr><td>No Duplicate Elements:</td> TreeSet does not allow duplicate elements. It ensures that each element in the set is unique, which can be useful in scenarios where duplicate values should be avoided. </tr><tr><td>Efficient Operations:</td> TreeSet provides efficient operations like insertion, deletion, and searching. These operations have a time complexity of O(log n), where n is the number of elements in the set. </tr><tr><td>Navigable Set Operations:</td> TreeSet provides additional navigational methods, such as higher(), lower(), ceiling(), and floor(), which allow you to find elements that are greater than, less than, or equal to a given value. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Overhead:</td> TreeSet requires additional memory to store the internal data structure, which can lead to higher memory overhead compared to other set implementations. </tr><tr><td>Slower Insertion and Removal:</td> Insertion and removal operations in TreeSet involve maintaining the sorted order of elements, which may require tree restructuring. It can make these operations slightly slower compared to HashSet or LinkedHashSet. </tr><tr><td>Limited Customization:</td> TreeSet is primarily designed for natural ordering or a single custom comparator. It may need more flexibility for multiple sorting criteria or complex sorting logic. </tr></ol> <p> <strong>Functions:</strong> </p> <ul> <tr><td>add(element):</td> Adds an element to the TreeSet while maintaining the sorted order. </tr><tr><td>remove(element):</td> Removes the specified element from the TreeSet. </tr><tr><td>contains(element):</td> Checks if the TreeSet contains the specified element. </tr><tr><td>size():</td> Returns the number of elements in the TreeSet. </tr><tr><td>first():</td> Returns the first (lowest) element in the TreeSet. </tr><tr><td>last():</td> Returns the last (highest) element in the TreeSet. </tr><tr><td>higher(element):</td> Returns the least element in the TreeSet that is strictly greater than the given element. </tr><tr><td>lower(element):</td> Returns the greatest element in the TreeSet that is strictly less than the given element. </tr></ul> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> TreeSetExample.java</p> <pre> import java.util.TreeSet; public class TreeSetExample { public static void main(String[] args) { // Create a TreeSet TreeSet numbers=new TreeSet(); // Add elements to the TreeSet numbers.add(5); numbers.add(2); numbers.add(8); numbers.add(1); numbers.add(4); // Print the TreeSet System.out.println('Elements in the TreeSet:'+numbers); // Check if an element exists System.out.println('Does TreeSet contain 4?'+numbers.contains(4)); // Remove an element numbers.remove(2); // Print the TreeSet after removal System.out.println('Elements in the TreeSet after removal:'+numbers); // Get the size of the TreeSet System.out.println('Size of the TreeSet:'+numbers.size()); // Get the first and last element System.out.println('First element:'+numbers.first()); System.out.println('Last element:'+numbers.last()); // Iterate over the TreeSet System.out.println('Iterating over the TreeSet:'); for (int number:numbers) { System.out.println(number); } } } </pre> <p> <strong>Output:</strong> </p> <pre> Elements in the TreeSet:[1, 2, 4, 5, 8] Does TreeSet contain 4? true Elements in the TreeSet after removal:[1, 4, 5, 8] Size of the TreeSet:4First element:1 Last element:8 Iterating over the TreeSet: 1 4 5 8 </pre> <h3>9) TreeMap:</h3> <p>TreeMap is a class in Java that implements the Map interface and provides a sorted key-value mapping based on the natural order of the keys or a custom comparator.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Sorted Ordering:</td> TreeMap maintains the keys in sorted order, which allows for efficient searching, retrieval, and range-based operations. </tr><tr><td>Key-Value Mapping:</td> TreeMap stores key-value pairs, enabling efficient lookup and retrieval of values based on the associated keys. </tr><tr><td>Red-Black Tree Implementation:</td> TreeMap uses a balanced binary search tree (Red-Black Tree) internally, ensuring efficient performance even for large datasets. </tr><tr><td>Support for Custom Comparators:</td> TreeMap allows the use of custom comparators to define the sorting order of the keys, providing flexibility in sorting criteria. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Memory Overhead:</td> TreeMap requires additional memory to store the internal tree structure and associated objects, resulting in higher memory usage compared to simpler data structures like HashMap. </tr><tr><td>Slower Insertion and Deletion:</td> Insertion and deletion operations in TreeMap have a time complexity of O(log n) due to the need for tree restructuring, making them slower compared to HashMap or LinkedHashMap. </tr><tr><td>Limited Performance for Unsorted Data:</td> TreeMap performs efficiently for sorted data, but its performance can degrade when dealing with unsorted data or frequent modifications, as it requires maintaining the sorted order. </tr></ol> <p> <strong>Functions:</strong> </p> <ul> <tr><td>put(key, value):</td> Inserts a key-value pair into the TreeMap. </tr><tr><td>get(key):</td> Retrieves the value associated with the specified key. </tr><tr><td>containsKey(key):</td> Checks if the TreeMap contains a specific key. </tr><tr><td>remove(key):</td> Removes the key-value pair associated with the specified key. </tr><tr><td>size():</td> Returns the number of key-value pairs in the TreeMap. </tr><tr><td>keySet():</td> Returns a set of all keys in the TreeMap. </tr><tr><td>values():</td> Returns a collection of all values in the TreeMap. </tr><tr><td>entrySet():</td> Returns a set of key-value pairs in the TreeMap. </tr></ul> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> TreeMapExample.java</p> <pre> import java.util.TreeMap; public class TreeMapExample { public static void main(String[] args) { // Create a TreeMap TreeMap scores=new TreeMap(); // Insert key-value pairs into the TreeMap scores.put('Alice',90); scores.put('Bob',80); scores.put('Charlie',95); scores.put('David',87); scores.put('Eve',92); //Access and print values from the TreeMap System.out.println('Score of Alice:'+scores.get('Alice')); System.out.println('Score of Charlie:'+scores.get('Charlie')); System.out.println('Score of David:'+scores.get('David')); // Update a value in the TreeMap scores.put('Bob',85); // Remove a key-value pair from the TreeMap scores.remove('Eve'); // Iterate through the TreeMap using a for-each loop System.out.println('Scores in the TreeMap:'); for (String name:scores.keySet()) { int score=scores.get(name); System.out.println(name+':'+score); } } } </pre> <p> <strong>Output:</strong> </p> <pre> Score of Alice:90 Score of Charlie:95 Score of David:87 Scores in the TreeMap: Alice:90 Bob:85 Charlie:95 David:87 </pre> <h3>10) Graph:</h3> <p>Graphs are data structure that represents a collection of interconnected nodes or vertices. They are composed of vertices and edges, where vertices represent entities and edges represent the relationships between those entities.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Versatility:</td> Graphs can represent a wide range of real-world scenarios, making them suitable for various applications such as social networks, transportation systems, and computer networks. </tr><tr><td>Relationship Representation:</td> Graphs provide a natural way to represent relationships and connections between entities, allowing for efficient analysis and traversal of these relationships. </tr><tr><td>Efficient Search and Traversal:</td> Graph algorithms like breadth-first search (BFS) and depth-first search (DFS) enable efficient traversal and searching of the graph's vertices and edges. </tr><tr><td>Modeling Complex Relationships:</td> Graphs can model complex relationships, including hierarchical structures, cyclic dependencies, and multiple connections between entities. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Space Complexity:</td> Graphs can consume a significant amount of memory, especially large-scale graphs with many vertices and edges. </tr><tr><td>The complexity of Operations:</td> Certain graph operations, such as finding the shortest path or detecting cycles, can have high time complexity, particularly in dense graphs. </tr><tr><td>Difficulty in Maintenance:</td> Modifying or updating a graph can be complex, as changes in the graph's structure may impact its connectivity and existing algorithms. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> GraphExample.java</p> <pre> import java.util.*; public class GraphExample { private int V; // Number of vertices private List<list> adjacencyList; // Adjacency list representation public GraphExample(int V) { this.V=V; adjacencyList=new ArrayList(V); // Initialize the adjacency list for (int i=0;i<v;i++) { adjacencylist.add(new arraylist()); } function to add an edge between two vertices public void addedge(int source,int destination) adjacencylist.get(source).add(destination); adjacencylist.get(destination).add(source); perform breadth-first search traversal of the graph bfs(int startvertex) boolean[] visited="new" boolean[v]; queue linkedlist(); visited[startvertex]="true;" queue.add(startvertex); while (!queue.isempty()) int currentvertex="queue.poll();" system.out.print(currentvertex+' '); list neighbors="adjacencyList.get(currentVertex);" for (int neighbor:neighbors) if (!visited[neighbor]) visited[neighbor]="true;" queue.add(neighbor); system.out.println(); depth-first dfs(int dfsutil(startvertex,visited); private dfsutil(int vertex,boolean[] visited) visited[vertex]="true;" system.out.print(vertex+' dfsutil(neighbor,visited); static main(string[] args) v="5;" number graphexample graphexample(v); edges graph.addedge(0,1); graph.addedge(0,2); graph.addedge(1,3); graph.addedge(2,3); graph.addedge(2,4); system.out.print('bfs traversal: graph.bfs(0); system.out.print('dfs graph.dfs(0); < pre> <p> <strong>Output:</strong> </p> <pre> BFS traversal: 0 1 2 3 4 DFS traversal: 0 1 3 2 4 </pre> <h3>11) Tree:</h3> <p>A tree is a widely used data structure in computer science that represents a hierarchical structure. It consists of nodes connected by edges, where each node can have zero or more child nodes.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Hierarchical Structure:</td> Trees provide a natural way to represent hierarchical relationships, such as file systems, organization charts, or HTML/XML documents. </tr><tr><td>Efficient Search:</td> Binary search trees enable efficient searching with a time complexity of O(log n), making them suitable for storing and retrieving sorted data. </tr><tr><td>Fast Insertion and Deletion:</td> Tree data structures offer efficient insertion and deletion operations, especially when balanced, such as AVL trees or Red-Black trees. </tr><tr><td>Ordered Iteration:</td> In-order traversal of a binary search tree gives elements in a sorted order, which is helpful for tasks like printing elements in sorted order or finding the next/previous element. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>High Memory Overhead:</td> Trees require additional memory to store node references or pointers, which can result in higher memory usage compared to linear data structures like arrays or lists. </tr><tr><td>Complex Implementation:</td> Implementing and maintaining a tree data structure can be more complex compared to other data structures like arrays or lists, especially for balanced tree variants. </tr><tr><td>Limited Operations:</td> Some tree variants, like binary search trees, do not support efficient operations like finding the kth smallest element or finding the rank of an element. </tr></ol> <p> <strong>Functions:</strong> </p> <ol class="points"> <tr><td>Insertion:</td> Add a new node to the tree. </tr><tr><td>Deletion:</td> Remove a node from the tree. </tr><tr><td>Search:</td> Find a specific node or element in the tree. </tr><tr><td>Traversal:</td> Traverse the tree in different orders, such as in-order, pre-order, or post-order. </tr><tr><td>Height/Depth:</td> Calculate the height or depth of the tree. </tr><tr><td>Balance:</td> Ensure the tree remains balanced to maintain efficient operations. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> TreeExample.java</p> <pre> import java.util.*; class TreeNode { int value; TreeNode left; TreeNode right; public TreeNode(int value) { this.value = value; left = null; right = null; } } public class TreeExample { public static void main(String[] args) { // Create a binary search tree TreeNode root = new TreeNode(50); root.left = new TreeNode(30); root.right = new TreeNode(70); root.left.left = new TreeNode(20); root.left.right = new TreeNode(40); root.right.left = new TreeNode(60); root.right.right = new TreeNode(80); // Perform common operations System.out.println('In-order Traversal:'); inOrderTraversal(root); System.out.println('
Search for value 40: '+search(root, 40)); System.out.println('Search for value 90: '+search(root, 90)); int minValue = findMinValue(root); System.out.println('Minimum value in the tree: '+minValue); int maxValue = findMaxValue(root); System.out.println('Maximum value in the tree: '+maxValue); } // In-order traversal: left subtree, root, right subtree public static void inOrderTraversal(TreeNode node) { if (node != null) { inOrderTraversal(node.left); System.out.print(node.value + ' '); inOrderTraversal(node.right); } } // Search for a value in the tree public static boolean search(TreeNode node, int value) { if (node == null) return false; if (node.value == value) return true; if (value <node.value) return search(node.left, value); else search(node.right, } find the minimum value in tree public static int findminvalue(treenode node) { if (node.left="=" null) node.value; findminvalue(node.left); maximum findmaxvalue(treenode (node.right="=" findmaxvalue(node.right); < pre> <p> <strong>Output:</strong> </p> <pre> In-order Traversal:20 30 40 50 60 70 80 Search for value 40: true Search for value 90: false Minimum value in the tree: 20 Maximum value in the tree: 80 </pre> <hr></node.value)></pre></v;i++)></list></pre></numbers.length;i++)> 2) 배열목록:
Java의 ArrayList는 요소의 저장 및 조작을 허용하는 동적 데이터 구조입니다. 이는 Java Collections Framework의 일부이며 내부적으로 배열을 사용하여 구현됩니다.
장점:
단점:
기능:
자바가 현재 날짜를 가져오는 중
구현:
파일 이름: ArrayListExample.java
import java.util.*; public class ArrayListExample { public static void main(String[] args) { // Create an ArrayList to store integers ArrayList numbers=new ArrayList(List.of(10,20,30,40,50)); //Access and print elements from the ArrayList System.out.println('Element at index 0:'+numbers.get(0)); System.out.println('Element at index 2:'+numbers.get(2)); System.out.println('Element at index 4:'+numbers.get(4)); // Calculate and print the sum of all elements in the ArrayList int sum=numbers.stream().mapToInt(Integer::intValue).sum(); System.out.println('Sum of ArrayList elements:'+sum); // Update an element in the ArrayList numbers.set(2,35); System.out.println('Updated element at index 2:'+numbers.get(2)); // Iterate through the ArrayList using a for-each loop and print the elements System.out.println('Elements in the ArrayList:'); for (int number:numbers) { System.out.println(number); } } } 산출:
f영화
Element at index 0:10 Element at index 2:30 Element at index 4:50 Sum of ArrayList elements:150 Updated element at index 2:35 Elements in the ArrayList: 10 20 35 40 50
3) 연결리스트:
연결된 목록은 요소가 노드라고 하는 별도의 개체에 저장되는 선형 데이터 구조입니다. 시퀀스의 다음 노드에 대한 참조 링크는 각 노드의 데이터 요소에 포함됩니다. 목록의 마지막 노드는 null로 연결되어 목록이 끝났음을 나타냅니다.
배열과 달리 연결된 목록에는 연속적인 메모리 할당이 필요하지 않습니다. 연결된 목록의 각 노드는 독립적으로 할당될 수 있으므로 동적 메모리 할당과 효율적인 삽입 및 삭제 작업이 가능합니다.
장점:
단점:
기능:
구현:
파일 이름: LinkedList1.java
import java.util.*; public class LinkedList1 { public static void main(String[] args) { // Create a LinkedList to store integers LinkedList linkedList1 = new LinkedList(); // Add elements to the LinkedList linkedList1.add(10); linkedList1.add(20); linkedList1.add(30); linkedList1.add(40); linkedList1.add(50); // Print the LinkedList System.out.println('LinkedList:'+linkedList1); // Remove an element from the LinkedList linkedList1.removeFirst(); System.out.println('LinkedList after removing first element:'+linkedList1); // Check if an element exists in the LinkedList boolean containsElement=linkedList1.contains(30); System.out.println('LinkedList contains element 30?'+containsElement); // Get the first and last elements of the LinkedList int firstElement=linkedList1.getFirst(); int lastElement=linkedList1.getLast(); System.out.println('First element:'+firstElement); System.out.println('Last element:'+lastElement); // Clear the LinkedList linkedList1.clear(); System.out.println('LinkedList after clearing:'+linkedList1); } } 산출:
LinkedList:[10, 20, 30, 40, 50] LinkedList after removing first element:[20, 30, 40, 50] LinkedList contains element 30?true First element:20 Last element:50 LinkedList after clearing:[]
4) 스택:
LIFO(후입선출) 원칙은 가장 최근에 삽입된 요소가 먼저 제거되는 요소이기도 함을 나타냅니다. 스택은 이 규칙을 따르는 선형 데이터 구조입니다. 'push' 및 'pop' 명령을 사용하여 스택에 요소를 추가하고 그에 따라 스택에서 최상위 요소를 제거합니다. 'peek' 기술을 사용하면 최상위 요소를 제거하지 않고도 해당 요소에 대한 액세스가 추가로 가능해집니다.
스택의 특징:
장점:
단점:
구현:
파일 이름: StackExample.java
import java.util.Stack; public class StackExample { public static void main(String[] args) { // Create a stack Stack stack=new Stack(); // Push elements onto the stack stack.push(10); stack.push(20); stack.push(30); // Print the top element of the stack System.out.println('Top element:'+stack.peek()); // Pop elements from the stack int poppedElement=stack.pop(); System.out.println('Popped element:'+poppedElement); // Check if the stack is empty System.out.println('Is stack empty?'+stack.isEmpty()); // Get the size of the stack System.out.println('Stack size:'+stack.size()); // Iterate over the stack System.out.println('Stack elements:'); for (Integer element:stack) { System.out.println(element); } } } 산출:
Top element:30 Popped element:30 Is stack empty?false Stack size:2 Stack elements: 10 20
5) 대기열:
큐는 FIFO(선입선출) 원칙을 따르는 Java의 선형 데이터 구조입니다. 요소가 뒤쪽에 삽입되고 앞쪽에서 제거되는 요소의 집합을 나타냅니다.
특징:
장점:
단점:
구현:
파일 이름: QueueExample.java
import java.util.Stack; import java.util.LinkedList; import java.util.Queue; public class QueueExample { public static void main(String[] args) { // Create a Queue to store integers Queue queue=new LinkedList(); // Enqueue elements to the Queue queue.offer(10); queue.offer(20); queue.offer(30); queue.offer(40); queue.offer(50); //Access and print the front element of the Queue System.out.println('Front element:'+queue.peek()); // Dequeue elements from the Queue and print them while (!queue.isEmpty()) { int element=queue.poll(); System.out.println('Dequeued element:'+element); } } } 산출:
Front element:10 Dequeued element:10 Dequeued element:20 Dequeued element:30 Dequeued element:40 Dequeued element:50
6) 해시맵:
HashMap은 키-값 쌍을 저장하고 검색하는 방법을 제공하는 Java의 데이터 구조입니다. 이는 Java Collections Framework의 일부이며 해시 테이블 데이터 구조를 기반으로 구현됩니다.
기능:
장점:
단점:
구현:
파일 이름: HashMapExample.java
import java.util.HashMap; public class HashMapExample { public static void main(String[] args) { // Create a HashMap to store String keys and Integer values HashMap hashMap=new HashMap(); // Add key-value pairs to the HashMap hashMap.put('John',25); hashMap.put('Alice',30); hashMap.put('Bob',35); //Access and print values based on keys System.out.println('Age of John:'+hashMap.get('John')); System.out.println('Age of Alice:'+hashMap.get('Alice')); // Check if a key exists in the HashMap System.out.println('Is Bob present?'+hashMap.containsKey('Bob')); // Update the value associated with a key hashMap.put('Alice',32); // Remove a key-value pair from the HashMap hashMap.remove('John'); // Print all key-value pairs in the HashMap System.out.println('Key-Value pairs in the HashMap:'); for (String key : hashMap.keySet()) { System.out.println(key+':'+hashMap.get(key)); } // Check the size of the HashMap System.out.println('Size of the HashMap:'+hashMap.size()); } } 산출:
Age of John:25 Age of Alice:30 Is Bob present?true Key-Value pairs in the HashMap: Bob:35 Alice:32 Size of the HashMap:2
7) 해시세트:
HashSet은 Set 인터페이스를 구현하고 해시 테이블에 요소를 저장하는 Java의 데이터 구조입니다.
특징:
장점:
단점:
연결을 얻다
구현:
파일 이름: HashSetExample.java
import java.util.HashSet; public class HashSetExample { public static void main(String[] args) { // Create a HashSet HashSet set=new HashSet(); // Add elements to the HashSet set.add('Apple'); set.add('Banana'); set.add('Orange'); set.add('Grapes'); set.add('Mango'); // Print the HashSet System.out.println('HashSet:'+set); // Check if an element exists System.out.println('Contains 'Apple':'+set.contains('Apple')); // Remove an element set.remove('Banana'); // Print the updated HashSet System.out.println('Updated HashSet:'+set); // Get the size of the HashSet System.out.println('Size of HashSet:'+set.size()); // Clear the HashSet set.clear(); // Check if the HashSet is empty System.out.println('Is HashSet empty?'+set.isEmpty()); } } 산출:
HashSet:[Apple, Grapes, Mango, Orange, Banana] Contains 'Apple':true Updated HashSet:[Apple, Grapes, Mango, Orange] Size of HashSet:4 Is HashSet empty?true
8) 트리세트:
TreeSet은 Red-Black 트리라고 하는 자체 균형 이진 검색 트리를 사용하여 요소를 정렬된 순서로 저장하는 Java의 SortedSet 인터페이스 구현입니다.
장점:
단점:
기능:
구현:
파일 이름: TreeSetExample.java
import java.util.TreeSet; public class TreeSetExample { public static void main(String[] args) { // Create a TreeSet TreeSet numbers=new TreeSet(); // Add elements to the TreeSet numbers.add(5); numbers.add(2); numbers.add(8); numbers.add(1); numbers.add(4); // Print the TreeSet System.out.println('Elements in the TreeSet:'+numbers); // Check if an element exists System.out.println('Does TreeSet contain 4?'+numbers.contains(4)); // Remove an element numbers.remove(2); // Print the TreeSet after removal System.out.println('Elements in the TreeSet after removal:'+numbers); // Get the size of the TreeSet System.out.println('Size of the TreeSet:'+numbers.size()); // Get the first and last element System.out.println('First element:'+numbers.first()); System.out.println('Last element:'+numbers.last()); // Iterate over the TreeSet System.out.println('Iterating over the TreeSet:'); for (int number:numbers) { System.out.println(number); } } } 산출:
Elements in the TreeSet:[1, 2, 4, 5, 8] Does TreeSet contain 4? true Elements in the TreeSet after removal:[1, 4, 5, 8] Size of the TreeSet:4First element:1 Last element:8 Iterating over the TreeSet: 1 4 5 8
9) 트리맵:
TreeMap은 Map 인터페이스를 구현하고 키의 자연 순서 또는 사용자 정의 비교기를 기반으로 정렬된 키-값 매핑을 제공하는 Java 클래스입니다.
장점:
단점:
기능:
np.어디
구현:
파일 이름: TreeMapExample.java
import java.util.TreeMap; public class TreeMapExample { public static void main(String[] args) { // Create a TreeMap TreeMap scores=new TreeMap(); // Insert key-value pairs into the TreeMap scores.put('Alice',90); scores.put('Bob',80); scores.put('Charlie',95); scores.put('David',87); scores.put('Eve',92); //Access and print values from the TreeMap System.out.println('Score of Alice:'+scores.get('Alice')); System.out.println('Score of Charlie:'+scores.get('Charlie')); System.out.println('Score of David:'+scores.get('David')); // Update a value in the TreeMap scores.put('Bob',85); // Remove a key-value pair from the TreeMap scores.remove('Eve'); // Iterate through the TreeMap using a for-each loop System.out.println('Scores in the TreeMap:'); for (String name:scores.keySet()) { int score=scores.get(name); System.out.println(name+':'+score); } } } 산출:
Score of Alice:90 Score of Charlie:95 Score of David:87 Scores in the TreeMap: Alice:90 Bob:85 Charlie:95 David:87
10) 그래프:
그래프는 상호 연결된 노드 또는 정점의 모음을 나타내는 데이터 구조입니다. 정점은 꼭지점과 모서리로 구성됩니다. 여기서 꼭지점은 엔터티를 나타내고 모서리는 해당 엔터티 간의 관계를 나타냅니다.
장점:
단점:
구현:
파일 이름: GraphExample.java
import java.util.*; public class GraphExample { private int V; // Number of vertices private List<list> adjacencyList; // Adjacency list representation public GraphExample(int V) { this.V=V; adjacencyList=new ArrayList(V); // Initialize the adjacency list for (int i=0;i<v;i++) { adjacencylist.add(new arraylist()); } function to add an edge between two vertices public void addedge(int source,int destination) adjacencylist.get(source).add(destination); adjacencylist.get(destination).add(source); perform breadth-first search traversal of the graph bfs(int startvertex) boolean[] visited="new" boolean[v]; queue linkedlist(); visited[startvertex]="true;" queue.add(startvertex); while (!queue.isempty()) int currentvertex="queue.poll();" system.out.print(currentvertex+\' \'); list neighbors="adjacencyList.get(currentVertex);" for (int neighbor:neighbors) if (!visited[neighbor]) visited[neighbor]="true;" queue.add(neighbor); system.out.println(); depth-first dfs(int dfsutil(startvertex,visited); private dfsutil(int vertex,boolean[] visited) visited[vertex]="true;" system.out.print(vertex+\' dfsutil(neighbor,visited); static main(string[] args) v="5;" number graphexample graphexample(v); edges graph.addedge(0,1); graph.addedge(0,2); graph.addedge(1,3); graph.addedge(2,3); graph.addedge(2,4); system.out.print(\'bfs traversal: graph.bfs(0); system.out.print(\'dfs graph.dfs(0); < pre> <p> <strong>Output:</strong> </p> <pre> BFS traversal: 0 1 2 3 4 DFS traversal: 0 1 3 2 4 </pre> <h3>11) Tree:</h3> <p>A tree is a widely used data structure in computer science that represents a hierarchical structure. It consists of nodes connected by edges, where each node can have zero or more child nodes.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Hierarchical Structure:</td> Trees provide a natural way to represent hierarchical relationships, such as file systems, organization charts, or HTML/XML documents. </tr><tr><td>Efficient Search:</td> Binary search trees enable efficient searching with a time complexity of O(log n), making them suitable for storing and retrieving sorted data. </tr><tr><td>Fast Insertion and Deletion:</td> Tree data structures offer efficient insertion and deletion operations, especially when balanced, such as AVL trees or Red-Black trees. </tr><tr><td>Ordered Iteration:</td> In-order traversal of a binary search tree gives elements in a sorted order, which is helpful for tasks like printing elements in sorted order or finding the next/previous element. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>High Memory Overhead:</td> Trees require additional memory to store node references or pointers, which can result in higher memory usage compared to linear data structures like arrays or lists. </tr><tr><td>Complex Implementation:</td> Implementing and maintaining a tree data structure can be more complex compared to other data structures like arrays or lists, especially for balanced tree variants. </tr><tr><td>Limited Operations:</td> Some tree variants, like binary search trees, do not support efficient operations like finding the kth smallest element or finding the rank of an element. </tr></ol> <p> <strong>Functions:</strong> </p> <ol class="points"> <tr><td>Insertion:</td> Add a new node to the tree. </tr><tr><td>Deletion:</td> Remove a node from the tree. </tr><tr><td>Search:</td> Find a specific node or element in the tree. </tr><tr><td>Traversal:</td> Traverse the tree in different orders, such as in-order, pre-order, or post-order. </tr><tr><td>Height/Depth:</td> Calculate the height or depth of the tree. </tr><tr><td>Balance:</td> Ensure the tree remains balanced to maintain efficient operations. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> TreeExample.java</p> <pre> import java.util.*; class TreeNode { int value; TreeNode left; TreeNode right; public TreeNode(int value) { this.value = value; left = null; right = null; } } public class TreeExample { public static void main(String[] args) { // Create a binary search tree TreeNode root = new TreeNode(50); root.left = new TreeNode(30); root.right = new TreeNode(70); root.left.left = new TreeNode(20); root.left.right = new TreeNode(40); root.right.left = new TreeNode(60); root.right.right = new TreeNode(80); // Perform common operations System.out.println('In-order Traversal:'); inOrderTraversal(root); System.out.println('
Search for value 40: '+search(root, 40)); System.out.println('Search for value 90: '+search(root, 90)); int minValue = findMinValue(root); System.out.println('Minimum value in the tree: '+minValue); int maxValue = findMaxValue(root); System.out.println('Maximum value in the tree: '+maxValue); } // In-order traversal: left subtree, root, right subtree public static void inOrderTraversal(TreeNode node) { if (node != null) { inOrderTraversal(node.left); System.out.print(node.value + ' '); inOrderTraversal(node.right); } } // Search for a value in the tree public static boolean search(TreeNode node, int value) { if (node == null) return false; if (node.value == value) return true; if (value <node.value) return search(node.left, value); else search(node.right, } find the minimum value in tree public static int findminvalue(treenode node) { if (node.left="=" null) node.value; findminvalue(node.left); maximum findmaxvalue(treenode (node.right="=" findmaxvalue(node.right); < pre> <p> <strong>Output:</strong> </p> <pre> In-order Traversal:20 30 40 50 60 70 80 Search for value 40: true Search for value 90: false Minimum value in the tree: 20 Maximum value in the tree: 80 </pre> <hr></node.value)></pre></v;i++)></list> 11) 나무:
트리는 계층 구조를 나타내는 컴퓨터 과학에서 널리 사용되는 데이터 구조입니다. 이는 가장자리로 연결된 노드로 구성되며, 각 노드는 0개 이상의 하위 노드를 가질 수 있습니다.
장점:
단점:
기능:
구현:
파일 이름: TreeExample.java
import java.util.*; class TreeNode { int value; TreeNode left; TreeNode right; public TreeNode(int value) { this.value = value; left = null; right = null; } } public class TreeExample { public static void main(String[] args) { // Create a binary search tree TreeNode root = new TreeNode(50); root.left = new TreeNode(30); root.right = new TreeNode(70); root.left.left = new TreeNode(20); root.left.right = new TreeNode(40); root.right.left = new TreeNode(60); root.right.right = new TreeNode(80); // Perform common operations System.out.println('In-order Traversal:'); inOrderTraversal(root); System.out.println('
Search for value 40: '+search(root, 40)); System.out.println('Search for value 90: '+search(root, 90)); int minValue = findMinValue(root); System.out.println('Minimum value in the tree: '+minValue); int maxValue = findMaxValue(root); System.out.println('Maximum value in the tree: '+maxValue); } // In-order traversal: left subtree, root, right subtree public static void inOrderTraversal(TreeNode node) { if (node != null) { inOrderTraversal(node.left); System.out.print(node.value + ' '); inOrderTraversal(node.right); } } // Search for a value in the tree public static boolean search(TreeNode node, int value) { if (node == null) return false; if (node.value == value) return true; if (value <node.value) return search(node.left, value); else search(node.right, } find the minimum value in tree public static int findminvalue(treenode node) { if (node.left="=" null) node.value; findminvalue(node.left); maximum findmaxvalue(treenode (node.right="=" findmaxvalue(node.right); < pre> <p> <strong>Output:</strong> </p> <pre> In-order Traversal:20 30 40 50 60 70 80 Search for value 40: true Search for value 90: false Minimum value in the tree: 20 Maximum value in the tree: 80 </pre> <hr></node.value)>