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Generating Compact Rough Cluster Descriptions Using an Evolutionary Algorithm

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Genetic and Evolutionary Computation – GECCO 2004 (GECCO 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3103))

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Abstract

Cluster analysis is a technique used to group objects into clusters such that similar objects are grouped together in the same cluster. Early methods were derived from multivariate statistics. Some newer methods are based on rough sets, introduced by Pawlak [3], [4]. An extension of rough sets to rough clusters was introduced in [5]. The lower approximation (LA) of a rough cluster contains objects that only belong to that cluster, and the upper approximation (UA) contains objects that may belong to more than one cluster. An EA can be used to find a set of lower approximations of rough clusters that provide the most comprehensive coverage of the data set with the minimum number of clusters.

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References

  1. Lingras, P.: Rough Set Clustering for Web Mining. In: Proceedings of 2002 IEEE International Conference on Fuzzy Systems (2002)

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  2. Nguyen, S.H.: Regularity Analysis and its Applications in Data Mining. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems, pp. 289–378. Physica-Verlag, Heidelberg (2000)

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  3. Pawlak, Z.: Rough Sets. International Journal of Information and Computer Sciences 11(5), 341–356 (1982)

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  4. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer, Boston (1991)

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  5. Voges, K.E., Pope, N.K.L., Brown, M.R.: Cluster Analysis of Marketing Data Examining On-line Shopping Orientation: A Comparison of K-means and Rough Clustering Approaches. In: Abbass, H.A., Sarker, R.A., Newton, C.S. (eds.) Heuristics and Optimization for Knowledge Discovery, pp. 207–224. Idea Group Publishing, Hershey (2002)

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Author information

Authors and Affiliations

  1. School of Management, University of Canterbury, Christchurch, New Zealand

    Kevin Voges

  2. School of Marketing, Griffith University, Nathan Campus, Brisbane, Australia

    Nigel Pope

Authors
  1. Kevin Voges
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  2. Nigel Pope
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Editor information

Editors and Affiliations

  1. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), Kanpur, 208016, Uttar Pradesh, India

    Kalyanmoy Deb

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© 2004 Springer-Verlag Berlin Heidelberg

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Voges, K., Pope, N. (2004). Generating Compact Rough Cluster Descriptions Using an Evolutionary Algorithm. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_152

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  • DOI: https://doi.org/10.1007/978-3-540-24855-2_152

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22343-6

  • Online ISBN: 978-3-540-24855-2

  • eBook Packages: Springer Book Archive

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