IR-ECLAT: a new algorithm for infrequent itemset mining

MUSTAFA MAN; NUR AQILAH BT RUSLAN; WAN AEZWANI WAN ABU BAKAR; JULAILY AIDA JUSOH; MOHD. KAMIR YUSOF; NUR LAILA NAJWA BT JOSDI

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IR-ECLAT: a new algorithm for infrequent itemset mining

Author(s)

MUSTAFA MAN Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu (UMT), 21030 Kuala Nerus, Terengganu, Malaysia

NUR AQILAH BT RUSLAN Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu (UMT), 21030 Kuala Nerus, Terengganu, Malaysia

WAN AEZWANI WAN ABU BAKAR Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA) Besut Campus, 22200 Besut, Terengganu, Malaysia

JULAILY AIDA JUSOH Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA) Besut Campus, 22200 Besut, Terengganu, Malaysia

MOHD. KAMIR YUSOF Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA) Besut Campus, 22200 Besut, Terengganu, Malaysia

NUR LAILA NAJWA BT JOSDI Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA) Besut Campus, 22200 Besut, Terengganu, Malaysia

Abstract

The key challenge of association rule mining (ARM) is to discover and extract a valuable information from databases. Mining valuable information from database could be very challenging especially for decisionmaking process. This is because mining association rule may require repetitious scanning of large dataset in the databases that can lead to high memory usage and time processing. A few algorithms were introduced by researchers to handle these related problems in data mining. The Incremental Equivalence Class Transformation (I-Eclat), Rare Incremental Equivalence Class Transformation (R-Eclat) algorithm are example of algorithms in rule mining techniques using vertical format data repositories for frequent and infrequent pattern mining. The main operation in I-Eclat and R-Eclat are intersecting tidset. Since the size of tidsets would affect the memory usage and its execution time, more memory and time required for a bigger tidsets. This paper introduces a new incremental of rare pattern mining approach by adopting R-Eclat called Incremental Rare Equivalence Class Transformation (IR-Eclat). The IR-Eclat specifically designed for infrequent pattern mining, and it is beneficial for dynamic database as the data is increasing towards volume with linear proportionate into time. In conjunction with big data explosion, the end users are at an advantage for the use of this incremental approach. The experimental results on several benchmark datasets indicate that IR-Eclat outperforms compare with R-Eclat by reducing its processing time especially in dynamic database as the data is increasing in volume from time to time.

Publication Details

Page(s) 3545-3551

DOI DOI not available

Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 11, Year: 2022

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Keywords

Data Mining, R-Eclat, Interestingness Measure, I-Eclat Model, Equivalence Class Transformation (Eclat), IR-Eclat

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