Abstract
Grouping data suffers from the curse of dimensionality and similarity functions that use all input features with equal relevance may not be effective and the features should be common to complete data. In this paper, Delaunay triangulation method is used to discover and cluster the subspace similarity-based data and finds the closest neighbors by similarity measures, and the triangulation drawing can be done repetitively over the space and cluster. This method avoids the risk of loss of information in any assumed distributed model, and it is a geometric model finds empty circles without any points, only the corner points of the triangles having related points.
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Ebinezar, Subashini, S., Stalin Alex, D., Subramanian, P. (2020). A Subspace Similarity-Based Data Clustering by Delaunay Triangulation. In: Saini, H., Sayal, R., Buyya, R., Aliseri, G. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 103. Springer, Singapore. https://doi.org/10.1007/978-981-15-2043-3_65
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DOI: https://doi.org/10.1007/978-981-15-2043-3_65
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