The communication network is expanding, and so the people are using the internet! Businesses are going digital for efficient management. The data generated on the internet is rising, and thus datasets are becoming complex. It is essential to organise, manage, access and analyse the data carefully and efficiently, a data structure is the most helpful technique, and the article focuses on the same!
In computer science, data structures are the basis for abstract data types (ADT), where ADT are the logical form of the data type. The physical layout of the data type is implemented using the data structure. Different data structure types are used for different kinds of applications; some are specialised in particular tasks.
The data structure is a collection of data values and relationships among them, operations and functions applicable to the data. It assists in organising, managing and storing data in a particular format. Thus, users can have easy access and modify the data efficiently.
Data structures help to manage large amounts of data, such as massive databases. Efficient algorithms are built based on efficient data structures. Besides efficient storage, data structures are also responsible for the efficient retrieval of information from stored memory. It includes an array, Linked List, Pointer, Searching, Stack, Graph, Queue, Structure, Programs, Sorting and so forth.
The article covers the concept of Searching in Data Structure and its methods. Two examples of algorithms are explained in detail to understand the concept clearly. To gain further knowledge, skills and expertise, online courses on data structure are available, mentioned at the end of the article.
What is Searching in Data Structure?
The process of finding the desired information from the set of items stored in the form of elements in the computer memory is referred to as ‘searching in data structure’. These sets of items are in various forms, such as an array, tree, graph, or linked list. Another way of defining searching in the data structure is by locating the desired element of specific characteristics in a collection of items.
Searching in the data structure can be done by implementing searching algorithms to check for or retrieve an element from any form of stored data structure. These algorithms are categorised based on their type of search operation, such as:
- Sequential search
The array or list of elements is traversed sequentially while checking every component of the set.
For example, Linear Search.
- Interval Search
Algorithms designed explicitly for searching in sorted data structures are included in the interval search. The efficiency of these algorithms is far better than linear search algorithms.
For example, Binary Search, Logarithmic Search.
These methods are examined based on the time taken by an algorithm to search an element matching the search item in the data collections and are given by,
- The best possible time
- The average time
- The worst-case time
The primary concerns are regarding worst-case times that lead to guaranteed predictions of the algorithm’s performance and are also easy to calculate compared to average times.
To illustrate examples and concepts in this article, ‘n’ items in the data collection in any data format are considered. Dominant operations are used to simplify analysis and algorithm comparison. For searching in a data structure, a comparison is a dominant operation, which is denoted by O() and pronounced as “big-Oh” or “Oh”.
There are numerous searching algorithms in a data structure such as linear search, binary search, interpolation search, jump search, exponential search, Fibonacci search, sublist search, the ubiquitous binary search, unbounded binary search, recursive function for substring search, and recursive program to search an element linearly in the given array. The article is restricted to linear and binary search algorithms and their working principles.
Let’s get detailed insight into the linear search and binary search in the data structure.
The linear search algorithm searches all elements in the array sequentially. Its best execution time is one, whereas the worst execution time is n, where n is the total number of items in the search array.
It is the most simple search algorithm in data structure and checks each item in the set of elements until it matches the search element until the end of data collection. When data is unsorted, a linear search algorithm is preferred.
Linear search has some complexities as given below:
- Space Complexity
Space complexity for linear search is O(n) as it does not use any extra space where n is the number of elements in an array.
- Time Complexity
*Best- case complexity = O(1) occurs when the search element is present at the first element in the search array.
*Worst- case complexity = O(n) occurs when the search element is not present in the set of elements or array.
*Average complexity = O(n) is referred to when the element is present somewhere in the search array.
Let’s take an array of elements as given below:
45, 78, 12, 67, 08, 51, 39, 26
To find ‘51’ in an array of 8 elements given above, a linear search algorithm will check each element sequentially till its pointer points to 51 in the memory space. It takes O(6) time to find 51 in an array. To find 12, in the above array, it takes O(3), whereas, for 26, it requires O(8) time.
This algorithm finds specific items by comparing the middlemost items in the data collection. When a match occurs, it returns the index of the item. When the middle item is greater than the item, it searches for a central item of the left sub-array. In contrast, if the middle item is smaller than the search item, it explores the middle of the item in the right sub-array. It continues searching for an item until it finds it or until the sub-arrays size becomes zero.
Binary search needs sorted order of items. It is faster than a linear search algorithm. It works on the divide and conquers principle.
Run-time complexity = O(log n)
The binary search algorithm has complexities as given below:
- Worst-case complexity = O (n log n)
- Average complexity = O (n log n)
- Best case complexity = O (1)
Let’s take a sorted algorithm of 08 elements:
08, 12, 26, 39, 45, 51, 67, 78
To find 51 in an array of the above elements,
The algorithm will divide an array into two arrays, 08, 12, 26, 39 and 45, 51, 67, 78
As 51 is greater than 39, it will start searching for elements on the array’s right side.
It will further divide the into two such as 45, 51 and 67, 78
As 51 is smaller than 67, it will start searching left of that sub-array.
That subarray is again divided into two as 45 and 51.
As 51 is the number matching to the search element, it will return its index number of that element in the array.
It will conclude that the search element 51 is located at the 6th position in an array.
Binary search reduces the time to half as the comparison count is reduced significantly than the linear search algorithm.
It is an improved variant of the binary search algorithm and works on the search element’s probing position. Similar to binary search algorithms, it works efficiently only on sorted data collection.
Worst execution time = O(n)
When the target element’s location is known in the data collection, an interpolation search is used. To find a number in the telephone directory, if one wants to search Monica’s telephone number, instead of using linear or binary search, one can directly probe to memory space storage where names start from ‘M’.
Searching in data structures refers to finding a given element in the array of ‘n’ elements. There are two categories, viz. Sequential search and interval search in searching. Almost all searching algorithms are based on one of these two categories. Linear and binary searches are the two simple and easy-to-implementing algorithms in which binary works faster than linear algorithms.
Though linear search is most straightforward, it checks each element until it finds a match to the search element, thus efficient when data collection is not sorted correctly. But, if the data collection is sorted and the length of an array is considerable, then binary search is faster.
The data structure is an essential part of computer programming while dealing with datasets. Programmers and developers need to keep updating and upskilling themselves with basics and updates in computer programming techniques. Programmers dealing with data structure should opt for courses often.
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