Abstract
This article describes a simple and fast algorithm that can automatically detect any number of well separated clusters, which may be of any shape e.g. convex and/or non-convex. This is in contrast to most of the existing clustering algorithms that assume a value for the number of clusters and/or a particular cluster structure. This algorithm is based on the principle that there is a definite threshold in the intra-cluster distances between nearest neighbors in the same cluster. Promising results on both real and artificial datasets have been included to show the effectiveness of the proposed technique.
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Sur, A., Chowdhury, A., Chowdhury, J.G., Das, S. (2013). Automatic Clustering Based on Cluster Nearest Neighbor Distance (CNND) Algorithm. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA). Advances in Intelligent Systems and Computing, vol 199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35314-7_22
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DOI: https://doi.org/10.1007/978-3-642-35314-7_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35313-0
Online ISBN: 978-3-642-35314-7
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