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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2011: Advances in Knowledge Discovery and Data Mining pp 493–505Cite as

Item Set Mining Based on Cover Similarity

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6635))

Abstract

While in standard frequent item set mining one tries to find item sets the support of which exceeds a user-specified threshold (minimum support) in a database of transactions, we strive to find item sets for which the similarity of their covers (that is, the sets of transactions containing them) exceeds a user-specified threshold. Starting from the generalized Jaccard index we extend our approach to a total of twelve specific similarity measures and a generalized form. We present an efficient mining algorithm that is inspired by the well-known Eclat algorithm and its improvements. By reporting experiments on several benchmark data sets we demonstrate that the runtime penalty incurred by the more complex (but also more informative) item set assessment is bearable and that the approach yields high quality and more useful item sets.

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Author information

Authors and Affiliations

  1. European Centre for Soft Computing, Calle Gonzalo Gutiérrez Quirós s/n, E-33600, Mieres, Asturias, Spain

    Marc Segond & Christian Borgelt

Authors
  1. Marc Segond
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  2. Christian Borgelt
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Editor information

Editors and Affiliations

  1. Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences, 518055, Shenzhen, China

    Joshua Zhexue Huang

  2. Faculty of Engineering and Information Technology, Center for Quantum Computation and Intelligent Systems, Data Sciences and Knowledge Discovery Lab, University of Technology Sydney, 2007, Sydney, NSW, Australia

    Longbing Cao

  3. Department of Computer Science and Engineering, University of Minnesota, 55455, Minneapolis, MN, USA

    Jaideep Srivastava

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© 2011 Springer-Verlag Berlin Heidelberg

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Segond, M., Borgelt, C. (2011). Item Set Mining Based on Cover Similarity. In: Huang, J.Z., Cao, L., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 6635. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20847-8_41

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  • DOI: https://doi.org/10.1007/978-3-642-20847-8_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20846-1

  • Online ISBN: 978-3-642-20847-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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