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.
This is a preview of subscription content, log in to check access.
The authors would like to thank the support from CONACyT Mexico through a scholarship to pursue doctoral studies at Unidad Cinvestav Tamaulipas.
Aloise, D., Deshpande, A., Hansen, P., Popat, P.: NP-hardness of euclidean sum-of-squares clustering. Mach. Learn. 75(2), 245–248 (2009)CrossRefGoogle Scholar
Bayá, A.E., Granitto, P.M.: How many clusters: a validation index for arbitrary-shaped clusters. IEEE/ACM Trans. Comput. Biol. Bioinform. 10(2), 401–414 (2013)CrossRefGoogle Scholar
Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Trans. Syst. Man Cybern. 38(1), 218–237 (2008)CrossRefGoogle Scholar
Hruschka, E.R., Campello, R.J.G.B., Freitas, A.A., de Carvalho, A.C.P.L.: A survey of evolutionary algorithms for clustering. IEEE Trans. Syst. Man Cybern. Part C 39(2), 133–155 (2009)CrossRefGoogle Scholar
Saha, S., Bandyopadhyay, S.: Some connectivity based cluster validity indices. Appl. Soft Comput. 12(5), 1555–1565 (2012)CrossRefGoogle Scholar
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)MathSciNetCrossRefzbMATHGoogle Scholar