relation: https://khub.utp.edu.my/scholars/10167/
title: FP-NoSQL: An Efficient Frequent Itemset Mining Algorithm Using the FP-DB Approach
creator: Chee, C.-H.
creator: Jaafar, J.
creator: Aziz, I.A.
description: 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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2018
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Chee, C.-H. and Jaafar, J. and Aziz, I.A.  (2018) FP-NoSQL: An Efficient Frequent Itemset Mining Algorithm Using the FP-DB Approach.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062802465&doi=10.1109%2fICBDAA.2018.8629737&partnerID=40&md5=0faf9f0d1f9aca8fce2806f1f56219fd
relation: 10.1109/ICBDAA.2018.8629737
identifier: 10.1109/ICBDAA.2018.8629737