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An Exhaustive Covering Approach to Parameter-Free Mining of Non-redundant Discriminative Itemsets

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Big Data Analytics and Knowledge Discovery (DaWaK 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9829))

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Abstract

Discriminative pattern mining is a promising extension of frequent pattern mining. This paper proposes an algorithm called ExCover, a shorthand for exhaustive covering, for finding non-redundant discriminative itemsets. ExCover outputs non-redundant patterns where each pattern covers best at least one positive transaction. With no control parameters limiting the search space, ExCover efficiently performs an exhaustive search for best-covering patterns using branch-and-bound pruning. During the search, candidate best-covering patterns are concurrently collected for each positive transaction. Formal discussions and experimental results exhibit that ExCover efficiently finds a more compact set of patterns in comparison with previous methods.

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Notes

  1. 1.

    In the previous example with the target class \(c=+\), a pattern \(\varvec{x}=\{\mathsf {D}\}\) is excluded, since \(p(\varvec{x}\mid c)=3/5=0.6\) and \(p(\varvec{x}\mid \lnot c)=5/5=1\).

  2. 2.

    Equivalent substitutions are also possible: \(p(c\mid \varvec{x}):=1\), \(p(\lnot \varvec{x}\mid \lnot c):=1\), and so on.

  3. 3.

    In other words, ExCover outputs all patterns having the same best score. We only exclude apparently redundant patterns to avoid the loss of crucial information.

  4. 4.

    Remind that \(\mathcal{D}_c(\varvec{x})\) denotes the set of positive transactions covered by a pattern \(\varvec{x}\).

  5. 5.

    More formally, a visited pattern is a pattern closed on the positives produced at Line 5 in the Grow procedure.

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Authors and Affiliations

  1. Department of Information Engineering, Meijo University, 1-501 Shiogama-guchi, Tenpaku-ku, Nagoya, 468-8502, Japan

    Yoshitaka Kameya

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  1. Yoshitaka Kameya
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Correspondence to Yoshitaka Kameya .

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Editors and Affiliations

  1. University of Science and Technology , Rolla, Missouri, USA

    Sanjay Madria

  2. Osaka University , Osaka, Japan

    Takahiro Hara

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Kameya, Y. (2016). An Exhaustive Covering Approach to Parameter-Free Mining of Non-redundant Discriminative Itemsets. In: Madria, S., Hara, T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2016. Lecture Notes in Computer Science(), vol 9829. Springer, Cham. https://doi.org/10.1007/978-3-319-43946-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-43946-4_10

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-43946-4

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