Abstract
Clustering is an exploratory data analysis tool that has gained enormous attention in the recent years specifically for gene expression data analysis.The K-means clustering is a method of cluster analysis which aims to partition n data points into K clusters. The K-means is possibly the best known and most widely used clustering technique. However, K-means does not necessarily find the optimal cluster configuration due to its significant sensitiveness in random selection of the initial cluster centers. On the other hand, MST-based clustering algorithm suffers from the selection of the inconsistent edges to produce quality clusters. In this paper, we present a novel method that bridges the K-means and the MST-based clustering algorithms. The proposed method not only overcomes the problem of random selection of the initial cluster centers for the former and the inconsistent edges for the later one but also automate them. We perform extensive experiments on the proposed method using both synthetic as well as the real world data sets. The experimental results show that the algorithm is able to produce desired clusters even for complex and high dimensional data points.
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Reddy, D., Mishra, D., Jana, P.K. (2011). MST-Based Cluster Initialization for K-Means. In: Meghanathan, N., Kaushik, B.K., Nagamalai, D. (eds) Advances in Computer Science and Information Technology. CCSIT 2011. Communications in Computer and Information Science, vol 131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17857-3_33
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DOI: https://doi.org/10.1007/978-3-642-17857-3_33
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