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Turning Krimp into a Triclustering Technique on Sets of Attribute-Condition Pairs that Compress

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Rough Sets (IJCRS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10314))

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Abstract

Mining ternary relations or triadic Boolean tensors is one of the recent trends in knowledge discovery that allows one to take into account various modalities of input object-attribute data. For example, in movie databases like IMBD, an analyst may find not only movies grouped by specific genres but see their common keywords. In the so called folksonomies, users can be grouped according to their shared resources and used tags. In gene expression analysis, genes can be grouped along with samples of tissues and time intervals providing comprehensible patterns. However, pattern explosion effects even with one more dimension are seriously aggravated. In this paper, we continue our previous study on searching for a smaller collection of “optimal” patterns in triadic data with respect to a set of quality criteria such as patterns’ cardinality, density, diversity, coverage, etc. We show how a simple data preprocessing has enabled us to use the frequent itemset mining algorithm Krimp based on MDL-principle for triclustering purposes.

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Notes

  1. 1.

    There exists a version of Krimp for mining linked relation tables, which seems to be suitable for n-ary relations as well [11].

  2. 2.

    FCA basics can found in books [5, 12] or tutorial [13].

  3. 3.

    http://bit.ly/triMLData.

  4. 4.

    http://www.alglib.net/.

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Acknowledgements

We would like to thank our colleagues, Rakesh Agrawal, Arno Siebes (for the introduction to Krimp), and Jean-François Boulicaut, for their piece of advice on pattern mining.

The article chapter was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy by the Russian Academic Excellence Project ‘5-100’. The second co-author was also supported by the Russian Foundation for Basic Research, grants no. 16-29-12982 and 16-01-00583.

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Authors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Maxim Yurov & Dmitry I. Ignatov

Authors
  1. Maxim Yurov
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  2. Dmitry I. Ignatov
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Correspondence to Dmitry I. Ignatov .

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Editors and Affiliations

  1. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Lech Polkowski

  2. University of Regina, Regina, Saskatchewan, Canada

    Yiyu Yao

  3. University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

  4. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  5. Southwest Jiaotong University, Chengdu, China

    Dun Liu

  6. Warsaw University, Warszawa, Poland

    Dominik Ślęzak

  7. Silesian University, Sosnowiec, Poland

    Beata Zielosko

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Yurov, M., Ignatov, D.I. (2017). Turning Krimp into a Triclustering Technique on Sets of Attribute-Condition Pairs that Compress. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10314. Springer, Cham. https://doi.org/10.1007/978-3-319-60840-2_40

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  • DOI: https://doi.org/10.1007/978-3-319-60840-2_40

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  • Online ISBN: 978-3-319-60840-2

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