Title: Maximal and closed frequent itemsets mining from uncertain database and data stream

Authors: Maliha Momtaz; Abu Ahmed Ferdaus; Chowdhury Farhan Ahmed; Mohammad Samiullah

Addresses: Department of Computer Science and Engineering, University of Dhaka, Dhaka, 1000, Bangladesh ' Department of Computer Science and Engineering, University of Dhaka, Dhaka, 1000, Bangladesh ' Department of Computer Science and Engineering, University of Dhaka, Dhaka, 1000, Bangladesh ' Faculty of Information Technology, Monash University, Melbourne, 3800, Australia

Abstract: Frequent itemsets (FIs) mining from uncertain database is a very popular research area nowadays. Many algorithms have been proposed to mine FI from uncertain database. But in typical FI mining process, all the FIs have to be mined individually, which needs a huge memory. Four trees are proposed in this paper which are: (i) maximal frequent itemset from uncertain database (MFU) tree which contains only the maximal frequent itemsets generated from uncertain database, (ii) closed frequent itemset from uncertain database (CFU) tree which contains only closed frequent itemsets generated from uncertain database, (iii) maximal frequent itemset from uncertain data stream (MFUS) tree which contains maximal frequent itemsets generated from uncertain data stream and (iv) closed frequent itemset from uncertain data stream (CFUS) tree which contains closed frequent itemsets generated from uncertain data stream. Experimental results are also presented which show that maximal and closed frequent itemsets mining requires less time and memory than typical frequent itemsets mining.

Keywords: FI; frequent itemset; uncertain database; FU; frequent itemset from uncertain database; MFI; maximal frequent itemset; CFI; closed frequent itemset; MFU; maximal frequent itemset from uncertain database; CFU; closed frequent itemset from uncertain database; FUS; frequent itemset from uncertain data stream; MFUS; maximal frequent itemset from uncertain data stream; CFUS; closed frequent itemset from uncertain data stream.

DOI: 10.1504/IJDS.2019.102792

International Journal of Data Science, 2019 Vol.4 No.3, pp.237 - 259

Received: 28 Jan 2019
Accepted: 07 Jul 2019
Published online: 07 Oct 2019 *

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