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

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.