An Improved UP-Growth High Utility Itemset Mining

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.

A General Method for mining high-Utility itemsets with correlated measures

“…Based on the pattern growth approach, Han et al extended the FP-Growth (Han et al, 2004) algorithm to develop the UP-Growth algorithm (Tseng et al, 2010) using the UP-Tree structure. Later, the authors improved that algorithm and introduced the UP-Growth+ algorithm (Srinivasa Rao and Krishna Prasad 2012). Then Liu and Qu (2012) proposed the HUI-Miner algorithm to mine HUIs using only one phase.…”