relation: https://khub.utp.edu.my/scholars/4276/
title: A semi-apriori algorithm for discovering the frequent itemsets
creator: Fageeri, S.O.
creator: Ahmad, R.
creator: Baharudin, B.B.
description: Mining the frequent itemsets are still one of the data mining research challenges. Frequent itemsets generation produce extremely large numbers of generated itemsets that make the algorithms inefficient. The reason is that the most traditional approaches adopt an iterative strategy to discover the itemsets, that's require very large process. Furthermore, the present mining algorithms cannot perform efficiently due to high and repeatedly database scan. In this paper we introduce a new binary-based Semi-Apriori technique that efficiently discovers the frequent itemsets. Extensive experiments had been carried out using the new technique, compared to the existing Apriori algorithms, a tentative result reveal that our technique outperforms Apriori algorithm in terms of execution time. © 2014 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2014
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Fageeri, S.O. and Ahmad, R. and Baharudin, B.B.  (2014) A semi-apriori algorithm for discovering the frequent itemsets.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938765772&doi=10.1109%2fICCOINS.2014.6868358&partnerID=40&md5=43d9806c0645660332a405f83c3f4dc0
relation: 10.1109/ICCOINS.2014.6868358
identifier: 10.1109/ICCOINS.2014.6868358