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A Fractal Fuzzy Approach to Clustering Tendency Analysis

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Advances in Artificial Intelligence – SBIA 2004 (SBIA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3171))

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Abstract

A hybrid system was implemented with the combination of Fractal Dimension Theory and Fuzzy Approximate Reasoning, in order to analyze datasets. In this paper, we describe its application in the initial phase of clustering methodology: the clustering tendency analysis. The Box-Counting Algorithm is carried out on a dataset, and with its resultant curve one obtains numeric indications related to the features of the dataset. Then, a fuzzy inference system acts upon these indications and produces information which enable the analysis mentioned above.

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Author information

Authors and Affiliations

  1. School of Electrical and Computer Engineering, Department of Computer Engineering and Industrial Automation, Unicamp – State University of Campinas, Campinas, SP, 13083-970, Brazil

    Sarajane Marques Peres & Márcio Luiz de Andrade Netto

  2. Department of Computer Science, Campus Cascavel, Unioeste – State University of Western of Paraná, Cascavel, PR, 85814-110, Brazil

    Sarajane Marques Peres

Authors
  1. Sarajane Marques Peres
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  2. Márcio Luiz de Andrade Netto
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Editor information

Editors and Affiliations

  1. Instituto de Informática, UFRGS, Porto Alegre, RS, Brasil

    Ana L. C. Bazzan

  2. Intelligent Systems Laboratory LSI, Center of Technology, Federal University of Maranao UFMA, Bacanga Campus, 65080-040, Sao Luis, MA, Brazil

    Sofiane Labidi

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Peres, S.M., de Andrade Netto, M.L. (2004). A Fractal Fuzzy Approach to Clustering Tendency Analysis. In: Bazzan, A.L.C., Labidi, S. (eds) Advances in Artificial Intelligence – SBIA 2004. SBIA 2004. Lecture Notes in Computer Science(), vol 3171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28645-5_40

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  • DOI: https://doi.org/10.1007/978-3-540-28645-5_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23237-7

  • Online ISBN: 978-3-540-28645-5

  • eBook Packages: Springer Book Archive

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