TY  - CONF
N1  - cited By 1; Conference of 2014 International Conference on Computer and Information Sciences, ICCOINS 2014 ; Conference Date: 3 June 2014 Through 5 June 2014; Conference Code:112912
N2  - 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.
ID  - scholars4276
TI  - A semi-apriori algorithm for discovering the frequent itemsets
KW  - Association rules; Iterative methods; Learning algorithms; Supports
KW  -  Apriori algorithms; Apriori techniques; Confidence; Frequent itemset; Iterative strategy; Mining algorithms; Research challenges; Traditional approaches
KW  -  Data mining
AV  - none
A1  - Fageeri, S.O.
A1  - Ahmad, R.
A1  - Baharudin, B.B.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938765772&doi=10.1109%2fICCOINS.2014.6868358&partnerID=40&md5=43d9806c0645660332a405f83c3f4dc0
Y1  - 2014///
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781479943913
ER  -