Updated March 8, 2023
Introduction to Binary Search Tree
The following article provides an outline for Binary Search Tree Advantages. A data structure with different nodes which is not actually linear and has different nodes to represent the organization of data in tree format is called Binary Search Tree or BST. Nodes are present in BST which has both left and right pointers where the nodes have keys and left side nodes should have keys less than the master node’s key value and right-side nodes should have keys more than the master node’s key value. The data is stored in an organized manner which can be easily accessed with the help of lookup tables and a dynamic set of values.
Various Binary Search Tree Advantages
Given below are the various advantages of binary search tree:
1. Data Storage
When data is stored in different nodes and arranged in a pattern, it is easy to remember the organized structure of data and this is the main advantage of BST. We can arrange the data in hierarchical fashion and make it useful for different aspects so that it explains the structural relationships between different nodes and how they are correlated with each other. While considering a particular dataset, it is taken as BST so that left and right nodes are arranged which depicts the relationship between the nodes and gives solution to any particular algorithm in the structure.
2. Representation
BST is mainly used to represent any form of relationships so that it shows us whether it is linked or sequential relationship between one another. The data is stored in the form of lists where nodes are not stored in any location but interconnected with each other and this links gives us the information about the data connected with each other. The node has right, left pointers and root nodes where root node give information about the entire BST and directs the other nodes in the form of data.
BST has also sequential form of representation where data is stored in the form of arrays which requires more space than linked representation. Size of array is determined by the number of nodes in BST and first index is the root node. This will give the values of left and right node values as indices. There is also chance of empty BST which will have index of 0.
3. Updating Data
It is easier to update data in the form of creation or deletion or any kind of editing in BST than in any form of lists or arrays. This makes users to depend on BST as it is easy to store data where less space is used and editing the data is made easy in the form of nodes at different points. This is particularly helpful where more data is present and chances of mistakes are more than in any common data structure. BST helps in understanding the structure of data and remove it if needed as in the field of Data science and Data engineering.
4. Data Access
This particularly helps users to access data from anywhere and it is faster than lists. The lists will be linked to each other and accessing data from lists will take time as it must connect with the related links and fetch the information. But it is slower than arrays as arrays need not check other arrays to fetch information of particular structure and arrays are arranged in structural manner.
BST makes it easier to store data and to move from one storage space to another whenever needed. This makes data storage and movement easy from one system to another. Data is stored in the form of nodes and hence misplacement of one node will result in collision of data elements in the nodes.
5. Nodes in BST
Depth and height of BST is determined with the help of nodes. The depth of one node is described as the number of nodes being connected to the master node. The node with most number of nodes has more depth in BST. Hence, this node will have more depth in the entire BST. And the depth of root node is 0 as there are no parental nodes for root node. Height of node starts in opposite direction i.e. from the leaf node and moves to the top. This makes the height of leaf node as 0 and root node as the number of leaf nodes it has. Maximum depth is the depth of BST and maximum height is the height of BST in n.
6. Data Search
Another advantage of BST is faster data search. When data search moves from one node to another, half of the subtree is eliminated which makes data search faster for the next set of nodes. Hence, we get expected results in half the time. It is calculated as 0(log2n) time to find an element in BST. There are some scenarios where it takes more time to fetch the element and formula to calculate the time is 0(n) where n is the number of nodes. Since left and right node elements are arranged in a particular fashion, as we move from one node to another, it becomes clear that which node has the data value.
7. Memory Blocks
Memory allocators and algorithms can be designed to make the data search easier in BST. These memory allocators help in finding the chunks of memory which has the desired data that also helps in data insertion by making the data chunks into smaller or larger size. This helps in allocating data based on our needs. Hence, it helps in maintaining an order for insertion of data in a faster manner. This also helps in finding data in a particular range by giving the name of two nodes in a BST. The data structure in BST is considered as an array and data is ordered.
Conclusion
It is important to have BST in a balanced manner or else data ordering and search will not happen in linear fashion which will incur more costs for implementing BST in the data flow. Also, while inserting data, one should be careful as it affects the structure of BST and hence the search of data.
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This is a guide to Binary Search Tree Advantages. Here we discuss the introduction and various binary search tree advantages respectively. You may also have a look at the following articles to learn more –
