Abstract
Clustering is task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. In this paper, we introduce hierarchical divisive clustering with multi view point based similarity measure. The hierarchical clustering is produced by the sequence of repeated bisections. The bisecting incremental k-means with multi view point based similarity measure is used in the clustering. We compare our approach with the existing algorithms on various document collections to verify the advantage of our proposed method.
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Jayaprada, S., Aswani, A., Gayathri, G. (2014). Hierarchical Divisive Clustering with Multi View-Point Based Similarity Measure. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013. Advances in Intelligent Systems and Computing, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-02931-3_55
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DOI: https://doi.org/10.1007/978-3-319-02931-3_55
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02930-6
Online ISBN: 978-3-319-02931-3
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