%0 Conference Paper
%A Chee, C.-H.
%A Jaafar, J.
%A Aziz, I.A.
%D 2018
%F scholars:10167
%I Institute of Electrical and Electronics Engineers Inc.
%K Competition; Data Analytics; Query languages; Query processing, Data analytics; Data set; Frequent itemset; Frequent itemset mining; Frequent pattern mining; Frequent patterns minings; Hidden patterns; Pattern database; Structure query languages, Data mining
%P 80-86
%R 10.1109/ICBDAA.2018.8629737
%T FP-NoSQL: An Efficient Frequent Itemset Mining Algorithm Using the FP-DB Approach
%U https://khub.utp.edu.my/scholars/10167/
%X One of the most favoured abilities of Information Technology (IT) among many Chief Executive Officials (CEOs) is data analytics. This is because it helps to increase the revenue, cut the cost, and gain more competitive advantage for their companies in the challenging business market. However, the hidden patterns of the frequent itemsets become more time consuming to be analysed when the amount of data is big in a data set. Furthermore, it also causes a large memory consumption for mining the hidden patterns of the frequent itemsets because of the heavy computation by the algorithm. Therefore, this research proposes FP-NoSQL as an efficient algorithm for Frequent Itemset Mining using the Not Only Structure Query Language (NoSQL) in a Frequent Pattern Database (FP-DB). The algorithm is capable of mining the hidden patterns of the frequent itemsets within a shorter run time although the amount of data is big in a data set. © 2018 IEEE.
%Z cited By 2; Conference of 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018 ; Conference Date: 21 November 2018 Through 22 November 2018; Conference Code:144703