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Evolutionary Clustering Using Multi-prototype Representation and Connectivity Criterion

  • Adán José-GarcíaEmail author
  • Wilfrido Gómez-Flores
Conference paper
  • 932 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10267)

Abstract

An automatic clustering approach based on differential evolution (DE) algorithm is presented. A clustering solution is represented by a new multi-prototype encoding scheme comprised of three parts: activation thresholds (binary values), cluster centroids (real values), and cluster labels (integer values). In addition, to measure the fitness of potential clustering solutions, an objective function based on a connectivity criterion is used. The performance of the proposed approach is compared with a DE-based automatic clustering technique as well as three conventional clustering algorithms (K-means, Ward, and DBSCAN). Several synthetic and real-life data sets having arbitrary-shaped clusters are considered. The experimental results indicate that the proposed approach outperforms its counterparts because it is capable to discover the actual number of clusters and the appropriate partitioning.

Keywords

Automatic clustering Differential evolution Non-linearly separable clusters Multi-prototype representation Cluster validity index 

Notes

Acknowledgments

The authors would like to thank the support from CONACyT Mexico through a scholarship to pursue doctoral studies at Unidad Cinvestav Tamaulipas.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Center for Research and Advanced Studies of the National Polytechnic InstituteCinvestav TamaulipasCiudad VictoriaMexico

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