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

Aligholipour O., KUNTALP M.

7th Annual International Conference on Information Management and Big Data, SIMBig 2020, Virtual, Online, 1 - 03 October 2020, pp.139-153 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1007/978-3-030-76228-5_10
  • City: Virtual, Online
  • Page Numbers: pp.139-153
  • Keywords: Cluster analysis, Cluster ensemble, Fuzzy C-means clustering, K-means clustering
  • Dokuz Eylül University Affiliated: Yes


© 2021, Springer Nature Switzerland AG.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.