Mining condensed sets of frequent episodes with more accurate frequencies from complex sequences

Gan, Min and Dai, Honghua 2012, Mining condensed sets of frequent episodes with more accurate frequencies from complex sequences, International journal of innovative computing, information & control, vol. 8, no. 1(A), pp. 453-470.

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Title Mining condensed sets of frequent episodes with more accurate frequencies from complex sequences
Author(s) Gan, Min
Dai, Honghua
Journal name International journal of innovative computing, information & control
Volume number 8
Issue number 1(A)
Start page 453
End page 470
Total pages 18
Publisher ICIC International
Place of publication [Kumamoto, Japan]
Publication date 2012-01
ISSN 1349-4198
Keyword(s) frequent episodes
condensed sets
sequence data mining
Summary Many previous approaches to frequent episode discovery only accept simple sequences. Although a recent approach has been able to nd frequent episodes from complex sequences, the discovered sets are neither condensed nor accurate. This paper investigates the discovery of condensed sets of frequent episodes from complex sequences. We adopt a novel anti-monotonic frequency measure based on non-redundant occurrences, and dene a condensed set, nDaCF (the set of non-derivable approximately closed frequent episodes) within a given maximal error bound of support. We then introduce a series of effective pruning strategies, and develop a method, nDaCF-Miner, for discovering nDaCF sets. Experimental results show that, when the error bound is somewhat high, the discovered nDaCF sets are two orders of magnitude smaller than complete sets, and nDaCF-miner is more efficient than previous mining approaches. In addition, the nDaCF sets are more accurate than the sets found by previous approaches.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890202 Application Tools and System Utilities
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2012, ICIC International
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044977

Document type: Journal Article
Collection: School of Information Technology
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