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An Enhanced Projected Clustering Algorithm for High Dimensional Space

B. Shanmugapriya, M. Punithavalli, G. Selvavinayagam


Clustering is a data mining technique for identifying groups in the data set based on some similarity measure. Clustering high dimensional data has been a major challenge due to the inherent sparsity of the points. Most existing clustering algorithms become substantially inefficient if the required similarity measure is computed between data points in the full dimensional space. A number of projected clustering algorithms have been proposed to overcome the above issue. This led to the development of a robust partitional distance based projected clustering algorithm based on K-means algorithm with the computation of distance restricted to subsets of attributes with dense object values. The algorithm is capable of detecting projected clusters of low dimensionality embedded in a high-dimensional space and avoids the computation of the distance in full-dimensional space. The algorithm has been demonstrated using synthetic and real datasets.


Clustering, High Dimensional Data, Projected Cluster, K-Means Clustering, Subspace Clustering

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