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Combining Rough Clustering Schemes as a Rough Ensemble

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Rough Sets and Knowledge Technology (RSKT 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9436))

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Abstract

One of the challenges of big data is to combine results of data mining obtained from a distributed dataset. The objective is to minimize the amount data transfer with minimum information loss. A generic combination process will not necessarily provide an optimal ensemble of results. In this paper, we describe a rough clustering problem that leads to a natural ordering of clusters. These ordered rough clusterings are then combined while preserving the properties of rough clustering. A time series dataset of commodity prices is clustered using two different representations to demonstrate the ordered rough clustering process. The information from the ordering of clusters is shown to help us retain salient aspects of individual rough clustering schemes.

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Authors and Affiliations

  1. Mathematics and Computing Science, Saint Maryś University, Halifax, Canada

    Pawan Lingras & Farhana Haider

Authors
  1. Pawan Lingras
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  2. Farhana Haider
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Corresponding author

Correspondence to Pawan Lingras .

Editor information

Editors and Affiliations

  1. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  2. Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

  3. Indian Statistical Institute, Kolkata, India

    Sushmita Mitra

  4. Zhejiang Ocean University, Zhejiang, China

    Wei-Zhi Wu

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Lingras, P., Haider, F. (2015). Combining Rough Clustering Schemes as a Rough Ensemble. In: Ciucci, D., Wang, G., Mitra, S., Wu, WZ. (eds) Rough Sets and Knowledge Technology. RSKT 2015. Lecture Notes in Computer Science(), vol 9436. Springer, Cham. https://doi.org/10.1007/978-3-319-25754-9_34

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  • DOI: https://doi.org/10.1007/978-3-319-25754-9_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25753-2

  • Online ISBN: 978-3-319-25754-9

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