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Tolerance Rough Set Theory Based Data Summarization for Clustering Large Datasets

  • Conference paper
Transactions on Rough Sets XIV

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 6600))

Abstract

Finding clusters in large datasets is an interesting challenge in many fields of Science and Technology. Many clustering methods have been successfully developed over the years. However, most of the existing clustering methods need multiple data scans to get converged. Therefore, these methods cannot be applied for cluster analysis in large datasets. Data summarization can be used as a pre-processing step to speed up classical clustering methods for large datasets. In this paper, we propose a data summarization scheme based on tolerance rough set theory termed rough bubble. Rough bubble utilizes leaders clustering method to collect sufficient statistics of the dataset, which can be used to cluster the dataset. We show that proposed summarization scheme outperforms recently introduced data bubble as a summarization scheme when agglomerative hierarchical clustering (single-link) method is applied to it. We also introduce a technique to reduce the number of distance computations required in leaders clustering method. Experiments are conducted with synthetic and real world datasets which show effectiveness of our methods for large datasets.

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Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, 781039, India

    Bidyut Kr. Patra & Sukumar Nandi

Authors
  1. Bidyut Kr. Patra
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  2. Sukumar Nandi
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Editor information

Editors and Affiliations

  1. University of Manitoba, R3T 5V6, Winnipeg, MB, Canada

    James F. Peters

  2. University of Warsaw, 02-097, Warsaw, Poland

    Andrzej Skowron  & Dominik Slezak  & 

  3. Kyushu Institute of Technology, 804, Tobata, Kitakyushu, Japan

    Hiroshi Sakai

  4. Jadavpur University, Kolkata, Indian Statistical Institute, Kolkata, India

    Mihir Kumar Chakraborty

  5. Cairo University, Orman, Giza, Egypt

    Aboul Ella Hassanien

  6. Zhangzhou Normal University, Fujian, China

    William Zhu

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© 2011 Springer-Verlag Berlin Heidelberg

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Patra, B.K., Nandi, S. (2011). Tolerance Rough Set Theory Based Data Summarization for Clustering Large Datasets. In: Peters, J.F., et al. Transactions on Rough Sets XIV. Lecture Notes in Computer Science, vol 6600. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21563-6_8

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  • DOI: https://doi.org/10.1007/978-3-642-21563-6_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21562-9

  • Online ISBN: 978-3-642-21563-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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