TELKOMNIKA Telecommunication, Computing, Electronics and Control
i-Eclat: performance enhancement of Eclat via incremental approach in frequent itemset mining

TELKOMNIKA Telecommunication, Computing, Electronics and Control
i-Eclat: performance enhancement of Eclat via incremental approach in frequent itemset mining

Association rule mining (ARM), Dense dataset, Eclat, Incremental-eclat (i-Eclat), Sparse dataset, Vertical format

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.

Wan Aezwani Wan Abu Bakar, Mustafa Man, Mahadi Man, Zailani Abdullah

DOI: 10.12928/TELKOMNIKA.v18i1.13497

Universitas Ahmad Dahlan

February 2020

Sri Wahyuni

ISSN: 1693-6930

http://journal.uad.ac.id/index.php/TELKOMNIKA

PDF

English

Text

TELKOMNIKA Telecommunication, Computing, Electronics and Control