An Approach Finding Frequent Items In Text Or Transactional Data Base By Using BST To Improve The Efficiency Of Apriori Algorithm

TL;DR

This paper introduces a binary search tree-based method to efficiently identify frequent items in transactional or text data, improving the Apriori algorithm's performance for data mining tasks.

Contribution

It proposes a novel BST-based approach for frequent item detection that enhances the efficiency of traditional algorithms like Apriori.

Findings

Improved efficiency in frequent item detection

Effective recognition of most and least frequent items

Applicability to both transactional and text data

Abstract

Data mining techniques have been widely used in various applications. Binary search tree based frequent items is an effective method for automatically recognize the most frequent items, least frequent items and average frequent items. This paper presents a new approach in order to find out frequent items. The word frequent item refers to how many times the item appeared in the given input. This approach is used to find out item sets in any order using familiar approach binary search tree. The method adapted here is in order to find out frequent items by comparing and incrementing the counter variable in existing transactional data base or text data. We are also representing different approaches in frequent item sets and also propose an algorithmic approach for the problem solving