Similarity Data Item Set Approach: An Encoded Temporal Data Base Technique

TL;DR

This paper compares various FP-growth algorithm variations for mining frequent item sets, demonstrating that the anti-FP-growth method is highly efficient and scalable, outperforming traditional algorithms like Apriori in speed and memory use.

Contribution

It introduces an anti-FP-growth algorithm that improves efficiency and scalability in frequent pattern mining, with a focus on application-specific information.

Findings

Anti-FP-growth is about ten times faster than Apriori.

The method is scalable for both long and short patterns.

It uses a tree structure to compress databases effectively.

Abstract

Data mining has been widely recognized as a powerful tool to explore added value from large-scale databases. Finding frequent item sets in databases is a crucial in data mining process of extracting association rules. Many algorithms were developed to find the frequent item sets. This paper presents a summary and a comparative study of the available FP-growth algorithm variations produced for mining frequent item sets showing their capabilities and efficiency in terms of time and memory consumption on association rule mining by taking application of specific information into account. It proposes pattern growth mining paradigm based FP-tree growth algorithm, which employs a tree structure to compress the database. The performance study shows that the anti- FP-growth method is efficient and scalable for mining both long and short frequent patterns and is about an order of magnitude faster than the Apriority algorithm and also faster than some recently reported new frequent-pattern mining.