Abstract :

Discovering Associations, Correlations And Regularities In Huge Transactional Data Is Made Possible By Frequent Pattern Mining In Data Mining. As Data Grows, Traditional Ways Like Apriori Quickly Use A Lot Of Power To Compute And Take Up A Lot Of Memory. The Performance Of FP-Growth, ECLAT And Their Advanced Versions Is Assessed And Compared In Dealing With Large Databases. We Suggest Applying A Method That Uses Both Tree-based And Vertical Data Structures To Help Decrease The Time Spent And Memory Used When Scanning. Following Experimental Testing On Benchmark Data, It Is Clear That The Hybrid Algorithm Is Clearly Superior To Standard Models When It Comes To Speed, The Ability To Scale With Large Datasets And The Completeness Of The Resulting Patterns. Keywords— Frequent Pattern Mining, FP-Growth, ECLAT, Large Databases, Association Rules, Data Mining Algorithms, Pattern Discovery, Transactional Data.

Published:

25-9-2025

Issue:

Vol. 25 No. 9 (2025)

Page Nos:

420-426

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite