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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control)
Mahadi Man
Universiti Malaysia Terengganu
Computer Science & IT Electrical & Electronics Engineering

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Frequent itemset mining: technique to improve eclat based algorithm Mahadi Man; Masita Abdul Jalil
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (671.508 KB) | DOI: 10.11591/ijece.v9i6.pp5471-5478

Abstract

In frequent itemset mining, the main challenge is to discover relationships between data in a transactional database or relational database. Various algorithms have been introduced to process frequent itemset. Eclat based algorithms are one of the prominent algorithm used for frequent itemset mining. Various researches have been conducted based on Eclat based algorithm such as Tidset, dEclat, Sortdiffset and Postdiffset. The algorithm has been improvised along the time. However, the utilization of physical memory and processing time become the main problem in this process. This paper reviews and presents a comparison of various Eclat based algorithms for frequent itemset mining and propose an enhancement technique of Eclat based algorithm to reduce processing time and memory usage. The experimental result shows some improvement in processing time and memory utilization in frequent itemset mining.
i-Eclat: performance enhancement of eclat via incremental approach in frequent itemset mining Wan Aezwani Wan Abu Bakar; Mustafa Man; Mahadi Man; Zailani Abdullah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 1: February 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i1.13497

Abstract

One example of the state-of-the-art vertical rule mining technique is called equivalence class transformation (Eclat) algorithm. Neither horizontal nor vertical data format, both are still suffering from the huge memory consumption. In response to the promising results of mining in a higher volume of data from a vertical format, and taking consideration of dynamic transaction of data in a database, the research proposes a performance enhancement of Eclat algorithm that relies on incremental approach called an Incremental-Eclat (i-Eclat) algorithm. Motivated from the fast intersection in Eclat, this algorithm of performance enhancement adopts via my structured query language (MySQL) database management system (DBMS) as its platform. It serves as the association rule mining database engine in testing benchmark frequent itemset mining (FIMI) datasets from online repository. The MySQL DBMS is chosen in order to reduce the preprocessing stages of datasets. The experimental results indicate that the proposed algorithm outperforms the traditional Eclat with 17% both in chess and T10I4D100K, 69% in mushroom, 5% and 8% in pumsb_star and retail datasets. Thus, among five (5) dense and sparse datasets, the average performance of i-Eclat is concluded to be 23% better than Eclat.
Co-Authors Masita Abdul Jalil Mustafa Man Wan Aezwani Wan Abu Bakar Zailani Abdullah