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Fuzzy Clustering with Prototype Extraction for Census Data Analysis

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Soft Computing: State of the Art Theory and Novel Applications

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 291))

Abstract

Not long ago primary census data became available to publicity. It opened qualitatively new perspectives not only for researchers in demography and sociology, but also for those people, who somehow face processes occurring in society.

In this paper authors propose using Data Mining methods for searching hidden interconnections in census data. A novel clustering-based technique is described as well. It allows determining factors which influence people behavior, in particular decision-making process (as an example, a decision whether to have a baby or not). Proposed technique concerns contrast mining as it is based on dividing the whole set of respondents on two contrasting groups. The first group consists of those, who possess a certain feature (for instance, has a baby) unlike members of the second group. We propose define clustering based subgroups out of the first group and their prototypes out of the second one. By means of analyzing subgroups’ and their prototypes’ characteristics it is possible to identify which factors influence the decision-making process. Authors also provide an experimental example of the described approach usage, which additionally shows that fuzzy clustering provides more accurate results than hard clustering techniques.

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Author information

Authors and Affiliations

  1. Applied Mathematics Department, National Technical University of Ukraine ”Kyiv Polytechnic Institute”, 37 Peremohy Prospekt, 03056, Kyiv, Ukraine

    Oleg Chertov & Marharyta Aleksandrova

Authors
  1. Oleg Chertov
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  2. Marharyta Aleksandrova
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Corresponding author

Correspondence to Oleg Chertov .

Editor information

Editors and Affiliations

  1. , Iona College, Machine Intelligence Institute, New Rochelle, 10801, New York, USA

    Ronald R. Yager

  2. Information Technologies of the, Republic of Azerbaijan, Ministry of Communications and, Zarifa Aliyeva str. 33, Baku, AZ1000, Azerbaijan

    Ali M. Abbasov

  3. , Department of Electrical, University of Alberta, ECERF Building W5-023, Edmonton, T6G 2V4, Alberta, Canada

    Marek Z. Reformat

  4. , Department of Information Technology, Azerbaijan Technical University, 25 H.Cavid Ave., Baku, AZ1073, Azerbaijan

    Shahnaz N. Shahbazova

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Chertov, O., Aleksandrova, M. (2013). Fuzzy Clustering with Prototype Extraction for Census Data Analysis. In: Yager, R., Abbasov, A., Reformat, M., Shahbazova, S. (eds) Soft Computing: State of the Art Theory and Novel Applications. Studies in Fuzziness and Soft Computing, vol 291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34922-5_20

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  • DOI: https://doi.org/10.1007/978-3-642-34922-5_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34921-8

  • Online ISBN: 978-3-642-34922-5

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