EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining

High-utility itemset mining (HUIM) is an important data mining task with wide applications. In this paper, we propose a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces several new ideas to more efficiently discovers high-utility itemsets both in terms of executio...

Full description

Saved in:
Bibliographic Details
Published inAdvances in Artificial Intelligence and Soft Computing Vol. 9413; pp. 530 - 546
Main Authors Zida, Souleymane, Fournier-Viger, Philippe, Lin, Jerry Chun-Wei, Wu, Cheng-Wei, Tseng, Vincent S.
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2015
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Artificial intelligence
Computer vision
Health & safety aspects of computing
High-utility mining
Itemset mining
Pattern mining
Online AccessGet full text
ISBN9783319270593
3319270591
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-27060-9_44

Cover

More Information
Summary:High-utility itemset mining (HUIM) is an important data mining task with wide applications. In this paper, we propose a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces several new ideas to more efficiently discovers high-utility itemsets both in terms of execution time and memory. EFIM relies on two upper-bounds named sub-tree utility and local utility to more effectively prune the search space. It also introduces a novel array-based utility counting technique named Fast Utility Counting to calculate these upper-bounds in linear time and space. Moreover, to reduce the cost of database scans, EFIM proposes efficient database projection and transaction merging techniques. An extensive experimental study on various datasets shows that EFIM is in general two to three orders of magnitude faster and consumes up to eight times less memory than the state-of-art algorithms d $$^2$$ HUP, HUI-Miner, HUP-Miner, FHM and UP-Growth+.
Bibliography:Original Abstract: High-utility itemset mining (HUIM) is an important data mining task with wide applications. In this paper, we propose a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces several new ideas to more efficiently discovers high-utility itemsets both in terms of execution time and memory. EFIM relies on two upper-bounds named sub-tree utility and local utility to more effectively prune the search space. It also introduces a novel array-based utility counting technique named Fast Utility Counting to calculate these upper-bounds in linear time and space. Moreover, to reduce the cost of database scans, EFIM proposes efficient database projection and transaction merging techniques. An extensive experimental study on various datasets shows that EFIM is in general two to three orders of magnitude faster and consumes up to eight times less memory than the state-of-art algorithms d\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^2$$\end{document}HUP, HUI-Miner, HUP-Miner, FHM and UP-Growth+.
ISBN:9783319270593
3319270591
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-27060-9_44