Mining Changes in Temporal Patterns in Latest Time Window for Knowledge Discovery

Sheel Shalini () and Kanhaiya Lal ()
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Sheel Shalini: Department of Computer Science and Engineering, Birla Institute of Technology Mesra, Patna Campus, Patna 800014, India
Kanhaiya Lal: Department of Computer Science and Engineering, Birla Institute of Technology Mesra, Patna Campus, Patna 800014, India

Journal of Information & Knowledge Management (JIKM), 2019, vol. 18, issue 03, 1-23

Abstract: Temporal Association Rule mining uncovers time integrated associations in a transactional database. However, in an environment where database is regularly updated, maintenance of rules is a challenging process. Earlier algorithms suggested for maintaining frequent patterns either suffered from the problem of repeated scanning or the problem of larger storage space. Therefore, this paper proposes an algorithm “Probabilistic Incremental Temporal Association Rule Mining (PITARM)” that uncovers the changed behaviour in an updated database to maintain the rules efficiently. The proposed algorithm defines two support measures to identify itemsets expected to be frequent in the successive segment in advance. It reduces unnecessary scanning of itemsets in the entire database through three-fold verification and avoids generating redundant supersets and power sets from infrequent itemsets. Implementation of pruning technique in incremental mining is a novel approach that makes it better than earlier incremental mining algorithms and consequently reduces search space to a great extent. It scans the entire database only once, thus reducing execution time. Experimental results confirm that it is an enhancement over earlier algorithms.

Keywords: Incremental mining; two support measures; temporal frequent itemsets; temporal association rule; candidate pruning (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S021964921950028X

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