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Performance analysis of algorithms for frequent pattern generation

Version 2 2024-06-03, 07:42
Version 1 2014-10-27, 16:38
conference contribution
posted on 2024-06-03, 07:42 authored by M Islam, Morshed Chowdhury, S Khan
Data mining refers to extracting or "mining" knowledge from large amounts of data. It is also called a method of "knowledge presentation" where visualization and knowledge representation techniques are used to present the mined knowledge to the user. Efficient algorithms to mine frequent patterns are crucial to many tasks in data mining. Since the Apriori algorithm was proposed in 1994, there have been several methods proposed to improve its performance. However, most still adopt its candidate set generation-and-test approach. In addition, many methods do not generate all frequent patterns, making them inadequate to derive association rules. The Pattern Decomposition (PD) algorithm that can significantly reduce the size of the dataset on each pass makes it more efficient to mine all frequent patterns in a large dataset. This algorithm avoids the costly process of candidate set generation and saves a large amount of counting time to evaluate support with reduced datasets. In this paper, some existing frequent pattern generation algorithms are explored and their comparisons are discussed. The results show that the PD algorithm outperforms an improved version of Apriori named Direct Count of candidates & Prune transactions (DCP) by one order of magnitude and is faster than an improved FP-tree named as Predictive Item Pruning (PIP). Further, PD is also more scalable than both DCP and PIP.

History

Pagination

43-55

Location

Cairns, Australia

Start date

2004-12-06

End date

2004-12-10

ISBN-13

9781876674960

ISBN-10

1876674962

Language

eng

Publication classification

E1 Full written paper - refereed, E Conference publication

Editor/Contributor(s)

Stonier R, Han Q, Li W

Title of proceedings

Complex 2004: Proceedings of the 7th Asia-Pacific Complex Systems Conference

Event

Asia-Pacific Complex Systems Conference (7th : 2004 : Cairns, Qld.)

Publisher

Central Queensland University

Place of publication

Rockhampton, Qld

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