Journal
Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2014.08.019
Keywords
Frequent Pattern Growth (FP Growth); Rapid Association Rule Mining (RARM); Data Mining; Frequent Patterns
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Pattern recognition is seen as a major challenge within the field of data mining and knowledge discovery. For the work in this paper, we have analyzed a range of widely used algorithms for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori algorithm, Frequent Pattern (FP) Growth algorithm, Rapid Association Rule Mining (RARM), ECLAT algorithm and Associated Sensor Pattern Mining of Data Stream (ASPMS) frequent pattern mining algorithms. This study also focuses on each of the algorithm's strengths and weaknesses for finding patterns among large item sets in database systems. (C) 2014 The Authors. Published by Elsevier B.V.
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