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

journal contribution
posted on 2012-01-01, 00:00 authored by Min Gan, Honghua Dai
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.

History

Journal

International journal of innovative computing, information & control

Volume

8

Issue

1(A)

Pagination

453 - 470

Publisher

ICIC International

Location

[Kumamoto, Japan]

ISSN

1349-4198

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2012, ICIC International

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