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Two-Class Fuzzy Clustering Ensemble Approach Based on a Constraint on Fuzzy Memberships

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Information Management and Big Data (SIMBig 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1410))

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Abstract

In recent years, the motivation to use the hybrid mixture of various methods has been increased. In this regard, the appropriate combination of supervised or unsupervised techniques have been proposed in order to enhances the performance of classification. In this paper, in order to obtain a stable fuzzy cluster scheme, a novel ensemble approach is presented. The proposed model consists of implementations of several Fuzzy C-means (FCM) based algorithms followed by the formation of a co-association matrix in relevant with the probability of each observation belonging to the clusters. The mean of these values is combined with a restriction criterion which have been designed to perceive the exact possibility of assigning observations to clusters. In other words, certain objects receive a reward, and uncertain objects with lower fuzzy coefficient degrees tend to be ineffective. Since partitioning clustering algorithms are commonly used as a consensus function, in this study, achieved row vector is given to K-means and FCM to generate final clusters. Several datasets have been used in order to evaluate the performance of the proposed model in comparison with different methods. Specially in internal validity indices, proposed method fulfills better results than traditional algorithms.

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Author information

Authors and Affiliations

  1. Dokuz Eylul University, Izmir, Turkey

    Omid Aligholipour & Mehmet Kuntalp

Authors
  1. Omid Aligholipour
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  2. Mehmet Kuntalp
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Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Juan Antonio Lossio-Ventura

  2. Visibilia, São Paulo, Brazil

    Jorge Carlos Valverde-Rebaza

  3. University of Valencia, Valencia, Spain

    Eduardo Díaz

  4. Universidad del Pacífico, Lima, Peru

    Hugo Alatrista-Salas

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Aligholipour, O., Kuntalp, M. (2021). Two-Class Fuzzy Clustering Ensemble Approach Based on a Constraint on Fuzzy Memberships. In: Lossio-Ventura, J.A., Valverde-Rebaza, J.C., Díaz, E., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2020. Communications in Computer and Information Science, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-76228-5_10

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  • DOI: https://doi.org/10.1007/978-3-030-76228-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-76227-8

  • Online ISBN: 978-3-030-76228-5

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