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Impact of Base Partitions on Multi-objective and Traditional Ensemble Clustering Algorithms

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9489))

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Abstract

This paper presents a comparative study of cluster ensemble and multi-objective cluster ensemble algorithms. Our aim is to evaluate the extent to which such methods are able to identify the underlying structure hidden in a data set, given different levels of information they receive as input in the set of base partitions (BP). To do so, given a gold/reference partition, we produced nine sets of BP containing properties of interest for our analysis, such as large number of subdivisions of true clusters. We aim at answering questions such as: are the methods able to generate new and more robust partitions than those in the set of BP? are the techniques influenced by poor quality partitions presented in the set of BP?

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Acknowledgments

This work was partially funded by FAPESP and a CAPES/ COFECUB project.

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Authors and Affiliations

  1. Universidade Federal de São Carlos, Sorocaba, Brazil

    Jane Piantoni, Katti Faceli, Tiemi C. Sakata & Julio C. Pereira

  2. University of Orleans, INSA Centre Val de Loire, LIFO EA 4022, Orléans, France

    Marcílio C. P. de Souto

Authors
  1. Jane Piantoni
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  2. Katti Faceli
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  3. Tiemi C. Sakata
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  4. Julio C. Pereira
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  5. Marcílio C. P. de Souto
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Corresponding author

Correspondence to Katti Faceli .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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Piantoni, J., Faceli, K., Sakata, T.C., Pereira, J.C., de Souto, M.C.P. (2015). Impact of Base Partitions on Multi-objective and Traditional Ensemble Clustering Algorithms. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_77

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  • DOI: https://doi.org/10.1007/978-3-319-26532-2_77

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26531-5

  • Online ISBN: 978-3-319-26532-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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