A New Approach for Subspace Clustering of High Dimensional Data

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Provided by: International Journal of Computer Science and Applications
Topic: Big Data
Format: PDF
Clustering high dimensional data is an emerging research area. The similarity criterion used by the traditional clustering algorithms is inadequate in high dimensional space. Also some of the dimensions are likely to be irrelevant thus hiding a possible clustering. Subspace clustering is an extension of traditional clustering that attempts to find clusters in different subspaces within a dataset. This paper proposes an idea by giving weight to every node of a cluster in a subspace. The cluster with greatest weight value will have more number of nodes when compared to all other clusters.
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