Computing Cluster Centers of Triangular Fuzzy Numbers using Innovative Metric Distance
S. Sreenivasan1, B. J. Balamurugan2
1S. Sreenivasan, School of Advanced Sciences, VIT University, Chennai Campus, Chennai, India.
2B. J. Balamurugan, School of Advanced Sciences, VIT University, Chennai Campus, Chennai, India.
Manuscript received on 21 August 2019. | Revised Manuscript received on 09 September 2019. | Manuscript published on 30 September 2019. | PP: 3378-3381 | Volume-8 Issue-11, September 2019. | Retrieval Number: J11400889019/2019©BEIESP | DOI: 10.35940/ijitee.J1140.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper we compute cluster centers of triangular fuzzy numbers through fuzzy c means clustering algorithm and kernel based fuzzy c means clustering algorithm. An innovative distance between the triangular fuzzy numbers is used and the distance is complete metric on triangular fuzzy numbers. The set of triangular fuzzy numbers and an another set with the same triangular fuzzy numbers by including an outlier or noisy point as an additional triangular fuzzy number are taken to find the cluster centers using MATLAB programming. An example is given to show the effectiveness between the algorithms.
Keywords: Fuzzy c means clustering algorithms, Fuzzy Clustering, Kernel function, Triangular fuzzy numbers.
Scope of the Article: Fuzzy Logics