Modified Improved Apriori Algorithm for Reduced Time Complexity

In today's era, transactional datasets are ubiquitous and continue to grow in size, making it increasingly challenging to mine frequent itemsets efficiently. The Apriori algorithm is a well-known method for mining frequent itemsets, but it suffers from high time complexity as the size of the da...

Full description

Saved in:
Bibliographic Details
Published in2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 pp. 1 - 5
Main Authors Tiwari, Geeta, Dubey, Shirish Mohan, Sharma, Gaurav, Bansal, Apporva
Format Conference Proceeding
LanguageEnglish
Published IEEE 09.04.2025
Subjects
Approximation algorithms
Enhanced Apriori Algorithm
Frequent item Introduction
Frequent Pattern Mining
Itemsets
Machine learning
Machine learning algorithms
Multicore processing
Parallel Computing
Parallel processing
Program processors
Runtime
Technological innovation
Time complexity
Transactional Dataset
Online AccessGet full text

Cover

More Information
Summary:In today's era, transactional datasets are ubiquitous and continue to grow in size, making it increasingly challenging to mine frequent itemsets efficiently. The Apriori algorithm is a well-known method for mining frequent itemsets, but it suffers from high time complexity as the size of the dataset increases. In this paper, we propose a multicore modified Apriori algorithm using machine learning to address this challenge. Our proposed algorithm leverages the parallel processing capability of modern multicore processors and integrates machine learning techniques to reduce the time complexity of the Apriori algorithm. The results of our experiments show that our proposed algorithm significantly outperforms other Apriori algorithms in terms of runtime, making it a promising solution for mining frequent itemsets from large transactional datasets.
DOI:10.1109/OTCON65728.2025.11070961