Article: Assessment of basic clustering techniques using teaching-learning-based optimisation Journal: International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP) 2016 Vol.5 No.2 pp.106 - 122 Abstract: There has been lot of talk regarding the initial cluster centre selection, because a bad centroid may result in malicious clustering. Due to this reason, we have taken the help of a latest population-based evolutionary optimisation technique called teaching-learning-based optimisation (TLBO) for selecting near about optimum cluster centres. After getting the finest initial centroids, we perform the necessary clustering by means of our proposed Enhanced clustering algorithm. In this paper, we have evaluated and assessed the performances of three different TLBO-based clustering algorithms: TLBO-supported classical K-means, TLBO-based fuzzy c-mean and our proposed approach of TLBO-based data clustering. Their clustering efficiency has been compared in conjunction with two typical cluster validity indices, namely the Davies-Bouldin's index and the Dunn's index. We extend our comparison by taking into account their calculated average quantisation error. Each algorithm is then tested on several datasets taken from UCI repository of machine learning databases. Experimental results show that our proposed approach produces better clustering with minimum quantisation error for most of the datasets as compared to the other discussed methods. Also the problem of initial centre selection is minimised to a greater extent. Inderscience Publishers - linking academia, business and industry through research

Title: Assessment of basic clustering techniques using teaching-learning-based optimisation

Authors: Bikram Keshari Mishra; Nihar Ranjan Nayak; Amiya Kumar Rath

Addresses: Department of Computer Science and Engineering, VSSUT, Burla, India ' Department of Computer Science and Engineering, Silicon Institute of Technology, Bhubaneswar, India ' Department of Computer Science and Engineering, VSSUT, Burla, India

Abstract: There has been lot of talk regarding the initial cluster centre selection, because a bad centroid may result in malicious clustering. Due to this reason, we have taken the help of a latest population-based evolutionary optimisation technique called teaching-learning-based optimisation (TLBO) for selecting near about optimum cluster centres. After getting the finest initial centroids, we perform the necessary clustering by means of our proposed Enhanced clustering algorithm. In this paper, we have evaluated and assessed the performances of three different TLBO-based clustering algorithms: TLBO-supported classical K-means, TLBO-based fuzzy c-mean and our proposed approach of TLBO-based data clustering. Their clustering efficiency has been compared in conjunction with two typical cluster validity indices, namely the Davies-Bouldin's index and the Dunn's index. We extend our comparison by taking into account their calculated average quantisation error. Each algorithm is then tested on several datasets taken from UCI repository of machine learning databases. Experimental results show that our proposed approach produces better clustering with minimum quantisation error for most of the datasets as compared to the other discussed methods. Also the problem of initial centre selection is minimised to a greater extent.

Keywords: cluster validity indices; K-means clustering; fuzzy c-means clustering; teaching-learning-based optimisation; TLBO; enhanced clustering; optimum cluster centres; performance evaluation; quantisation errors; initial centre selection.

DOI: 10.1504/IJKESDP.2016.075977

International Journal of Knowledge Engineering and Soft Data Paradigms, 2016 Vol.5 No.2, pp.106 - 122

Received: 25 Jun 2015
Accepted: 29 Sep 2015
Published online: 20 Apr 2016 *

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Title: Assessment of basic clustering techniques using teaching-learning-based optimisation

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