chapter

Tolerance rough set theory based data summarization for clustering large datasets

Authors:

Bidyut Kr. Patra,

Sukumar NandiAuthors Info & Claims

Transactions on rough sets XIV

January 2011

Pages 139 - 158

Published: 01 January 2011 Publication History

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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Cited By

Ahmed M(2019)Data summarizationKnowledge and Information Systems10.1007/s10115-018-1183-058:2(249-273)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10115-018-1183-0
Hu CLuo NZhao Q(2016)Fast fuzzy trajectory clustering strategy based on data summarization and rough approximationCluster Computing10.1007/s10586-016-0603-819:3(1411-1420)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1007/s10586-016-0603-8

Index Terms

Tolerance rough set theory based data summarization for clustering large datasets
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Guide books

Transactions on rough sets XIV

January 2011

233 pages

ISBN:9783642215629

Editors:
James F. Peters
University of Manitoba, Winnipeg, MB, Canada
,
Andrzej Skowron
University of Warsaw, Poland
,
Hiroshi Sakai
Kyushu Institute of Technology, Tobata, Kitakyushu, Japan
,
Mihir Kumar Chakraborty
Jadavpur University and Indian Statistical Institute, Calcutta, India
,
Dominik Slezak
University of Warsaw, Poland

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 January 2011

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Citations

Cited By

Ahmed M(2019)Data summarizationKnowledge and Information Systems10.1007/s10115-018-1183-058:2(249-273)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10115-018-1183-0
Hu CLuo NZhao Q(2016)Fast fuzzy trajectory clustering strategy based on data summarization and rough approximationCluster Computing10.1007/s10586-016-0603-819:3(1411-1420)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1007/s10586-016-0603-8

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