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Original scientific paper
https://doi.org/10.17535/crorr.2014.0004

A new fusion algorithm for fuzzy clustering

Ivan Vidović ; Faculty of Electrical Engineering Osijek, Josip Juraj Strossmayer University of Osijek
Dražen Bajer ; Faculty of Electrical Engineering Osijek, Josip Juraj Strossmayer University of Osijek
Rudolf Scitovski ; Department of Mathematics, Josip Juraj Strossmayer University of Osijek

Fulltext: english, pdf (806 KB) pages 149-159 downloads: 353* cite
APA 6th Edition
Vidović, I., Bajer, D. & Scitovski, R. (2014). A new fusion algorithm for fuzzy clustering. Croatian Operational Research Review, 5 (2), 149-159. https://doi.org/10.17535/crorr.2014.0004
MLA 8th Edition
Vidović, Ivan, et al. "A new fusion algorithm for fuzzy clustering." Croatian Operational Research Review, vol. 5, no. 2, 2014, pp. 149-159. https://doi.org/10.17535/crorr.2014.0004. Accessed 6 Dec. 2019.
Chicago 17th Edition
Vidović, Ivan, Dražen Bajer and Rudolf Scitovski. "A new fusion algorithm for fuzzy clustering." Croatian Operational Research Review 5, no. 2 (2014): 149-159. https://doi.org/10.17535/crorr.2014.0004
Harvard
Vidović, I., Bajer, D., and Scitovski, R. (2014). 'A new fusion algorithm for fuzzy clustering', Croatian Operational Research Review, 5(2), pp. 149-159. https://doi.org/10.17535/crorr.2014.0004
Vancouver
Vidović I, Bajer D, Scitovski R. A new fusion algorithm for fuzzy clustering. Croatian Operational Research Review [Internet]. 2014 [cited 2019 December 06];5(2):149-159. https://doi.org/10.17535/crorr.2014.0004
IEEE
I. Vidović, D. Bajer and R. Scitovski, "A new fusion algorithm for fuzzy clustering", Croatian Operational Research Review, vol.5, no. 2, pp. 149-159, 2014. [Online]. https://doi.org/10.17535/crorr.2014.0004

Abstracts
In this paper, we have considered the merging problem of two ellipsoidal clusters in order to construct a new fusion algorithm for fuzzy clustering. We have proposed a criterion for merging two ellipsoidal clusters ∏1, ∏2 with associated main Mahalanobis circles Ej(cj,σj), where cj is the centroid and σ^2j is the Mahalanobis variance of cluster ∏j . Based on the well-known Davies-Bouldin index, we have constructed a new fusion algorithm. The criterion has been tested on several data sets, and the performance of the fusion algorithm has been demonstrated on an illustrative example. 

Keywords
fusion algorithm; fuzzy clustering; Mahalanobis clustering; cluster merging; Davies-Bouldin index

Hrčak ID: 133693

URI
https://hrcak.srce.hr/133693

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