Association Rule Mining Based On Trade List

TL;DR

This paper introduces a new association rule mining algorithm that improves efficiency by scanning the database only once and using a Trade List to generate frequent item sets and rules, reducing time and space compared to Apriori.

Contribution

The proposed algorithm enhances mining efficiency by minimizing database scans and utilizing a Trade List, offering a faster alternative to traditional methods like Apriori.

Findings

Reduces database scans to once, improving speed.

Uses Trade List to efficiently generate frequent item sets.

Outperforms traditional algorithms in large databases.

Abstract

In this paper a new mining algorithm is defined based on frequent item set. Apriori Algorithm scans the database every time when it finds the frequent item set so it is very time consuming and at each step it generates candidate item set. So for large databases it takes lots of space to store candidate item set .In undirected item set graph, it is improvement on apriori but it takes time and space for tree generation. The defined algorithm scans the database at the start only once and then from that scanned data base it generates the Trade List. It contains the information of whole database. By considering minimum support it finds the frequent item set and by considering the minimum confidence it generates the association rule. If database and minimum support is changed, the new algorithm finds the new frequent items by scanning Trade List. That is why it's executing efficiency is improved distinctly compared to traditional algorithm.