Fuzzy Clustering with Prototype Extraction for Census Data Analysis

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


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.


Fuzzy Cluster Data Mining Technique Subtractive Cluster Subtractive Algorithm Membership Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Applied Mathematics DepartmentNational Technical University of Ukraine ”Kyiv Polytechnic Institute”KyivUkraine

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