An Improved UP-Growth High Utility Itemset Mining

TL;DR

This paper introduces an improved algorithm for high utility itemset mining that reduces execution time by optimizing the UP-Growth process using the UP Tree data structure.

Contribution

It presents a modified UP-Growth algorithm that enhances efficiency and reduces execution time in high utility itemset mining using the UP Tree structure.

Findings

Reduces execution time of high utility itemset mining

Efficiently manages transaction data with UP Tree

Improves performance over existing UP-Growth methods

Abstract

Efficient discovery of frequent itemsets in large datasets is a crucial task of data mining. In recent years, several approaches have been proposed for generating high utility patterns, they arise the problems of producing a large number of candidate itemsets for high utility itemsets and probably degrades mining performance in terms of speed and space. Recently proposed compact tree structure, viz., UP Tree, maintains the information of transactions and itemsets, facilitate the mining performance and avoid scanning original database repeatedly. In this paper, UP Tree (Utility Pattern Tree) is adopted, which scans database only twice to obtain candidate items and manage them in an efficient data structured way. Applying UP Tree to the UP Growth takes more execution time for Phase II. Hence this paper presents modified algorithm aiming to reduce the execution time by effectively identifying high utility itemsets.