Efficient Mining of Non-Redundant Periodic Frequent Patterns

Author:

Afriyie Michael Kofi1,Nofong Vincent Mwintieru1,Wondoh John2,Abdel-Fatao Hamidu1

Affiliation:

1. Computer Science and Engineering Department, University of Mines and Technology, P. O. Box 237, Tarkwa, Ghana

2. University of South of Australia, Mawson Lakes Campus, Adelaide, Australia

Abstract

Periodic frequent patterns are frequent patterns which occur at periodic intervals in databases. They are useful in decision making where event occurrence intervals are vital. Traditional algorithms for discovering periodic frequent patterns, however, often report a large number of such patterns, most of which are often redundant as their periodic occurrences can be derived from other periodic frequent patterns. Using such redundant periodic frequent patterns in decision making would often be detrimental, if not trivial. This paper addresses the challenge of eliminating redundant periodic frequent patterns by employing the concept of deduction rules in mining and reporting only the set of non-redundant periodic frequent patterns. It subsequently proposes and develops a Non-redundant Periodic Frequent Pattern Miner (NPFPM) to achieve this purpose. Experimental analysis on benchmark datasets shows that NPFPM is efficient and can effectively prune the set of redundant periodic frequent patterns.

Publisher

World Scientific Pub Co Pte Ltd

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