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Abstract
Clustering is the act of assembling N data points into K clusters in order that, data points inside the same cluster are analogous, while data points in unlike clusters are dissimilar from each other. The majority of clustering algorithms befall ineffective when unsuitable parameters are provided, or implemented on datasets which are consist of clusters with varied form, dimension, and masses. To lessen these insufficiencies, we propose a new split-and-merge clustering methodology. In which an n-MST (Minimum Spanning Tree) is formed to lead the splitting and merging process. The proposed method doesn’t need any prior domain knowledge of dataset. Experimental consequences exhibit its efficiency on real datasets.
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Journal of Information Sciences, Volume 181 Issue 16,August 2011, Elsevier ScienceInc.New York,USA,pages:3397-3410.
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