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Efficiently Depth-First Minimal Pattern Mining

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Advances in Knowledge Discovery and Data Mining (PAKDD 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8443))

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Abstract

Condensed representations have been studied extensively for 15 years. In particular, the maximal patterns of the equivalence classes have received much attention with very general proposals. In contrast, the minimal patterns remained in the shadows in particular because of their difficult extraction. In this paper, we present a generic framework for minimal patterns mining by introducing the concept of minimizable set system. This framework addresses various languages such as itemsets or strings, and at the same time, different metrics such as frequency. For instance, the free and the essential patterns are naturally handled by our approach, just as the minimal strings. Then, for any minimizable set system, we introduce a fast minimality check that is easy to incorporate in a depth-first search algorithm for mining the minimal patterns. We demonstrate that it is polynomial-delay and polynomial-space. Experiments on traditional benchmarks complete our study.

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Author information

Authors and Affiliations

  1. Université François Rabelais Tours, LI, 3 Place Jean Jaurès, F-41029, Blois, France

    Arnaud Soulet

  2. Université de Caen, GREYC, Campus Côte de Nacre, F-14032, Caen Cédex, France

    François Rioult

Authors
  1. Arnaud Soulet
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  2. François Rioult
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Editor information

Editors and Affiliations

  1. National Cheng Kung University, Tainan, Taiwan, R.O.C.

    Vincent S. Tseng  & Hung-Yu Kao  & 

  2. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Tu Bao Ho

  3. Nanjing University, China

    Zhi-Hua Zhou

  4. National Chengchi University, Taipei, Taiwan, R.O.C.

    Arbee L. P. Chen

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© 2014 Springer International Publishing Switzerland

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Soulet, A., Rioult, F. (2014). Efficiently Depth-First Minimal Pattern Mining. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8443. Springer, Cham. https://doi.org/10.1007/978-3-319-06608-0_3

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  • DOI: https://doi.org/10.1007/978-3-319-06608-0_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06607-3

  • Online ISBN: 978-3-319-06608-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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