Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Syarilla Iryani Ahmad Saany
Universiti Sultan Zainal Abidin
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Analysis study on R-Eclat algorithm in infrequent itemsets mining Mustafa Man; Julaily Aida Jusoh; Syarilla Iryani Ahmad Saany; Wan Aezwani Wan Abu Bakar; Mohd Hafizuddin Ibrahim
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 (653.478 KB) | DOI: 10.11591/ijece.v9i6.pp5446-5453

Abstract

There are rising interests in developing techniques for data mining. One of the important subfield in data mining is itemset mining, which consists of discovering appealing and useful patterns in transaction databases. In a big data environment, the problem of mining infrequent itemsets becomes more complicated when dealing with a huge dataset. Infrequent itemsets mining may provide valuable information in the knowledge mining process. The current basic algorithms that widely implemented in infrequent itemset mining are derived from Apriori and FP-Growth. The use of Eclat-based in infrequent itemset mining has not yet been extensively exploited. This paper addresses the discovery of infrequent itemsets mining from the transactional database based on Eclat algorithm. To address this issue, the minimum support measure is defined as a weighted frequency of occurrence of an itemsets in the analysed data. Preliminary experimental results illustrate that Eclat-based algorithm is more efficient in mining dense data as compared to sparse data.
Co-Authors Julaily Aida Jusoh Mohd Hafizuddin Ibrahim Mustafa Man Wan Aezwani Wan Abu Bakar