文章基本信息

标题：i-Eclat: performance enhancement of eclat via incremental approach in frequent itemset mining
本地全文：下载
作者：Wan Aezwani Wan Abu Bakar ; Mustafa Man ; Mahadi Man 等
期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2020
卷号：18
期号：1
页码：562-570
DOI：10.12928/telkomnika.v18i1.13497
出版社：Universitas Ahmad Dahlan
摘要：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.
关键词：association rule mining (ARM); dense dataset; Eclat; incremental-Eclat (i-Eclat); sparse dataset; vertical format;