Open AccessAnalysis study on R-Eclat algorithm in infrequent itemsets mining
Author(s) -
Mustafa Man,
Julaily Aida Jusoh,
Syarilla Iryani Ahmad Saany,
Wan Azelee Wan Abu Bakar,
Mohd Hafizuddin Ibrahim
Publication year - 2019
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v9i6.pp5446-5453
Subject(s) - data mining , computer science , database transaction , transaction data , association rule learning , apriori algorithm , process (computing) , a priori and a posteriori , knowledge extraction , transaction processing , measure (data warehouse) , algorithm , database , philosophy , epistemology , operating system
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.