Clustering is an unsupervised process of grouping elements together, so that elements assigned to the same cluster are more similar to each other than to the remaining data points. High-dimensional data takes place in many fields. Clustering process is because of sparsity, also growing complexity in unique distances between data points. Here capture an original perception on the trouble of clustering high-dimensional data. To neglect the curse of dimensionality by scrutinizing a lower dimensional feature subspace, hold dimensionality by taking advantage of inherently high-dimensional phenomena. Exclusively, using hubness. Validate our hypothesis by demonstrating that hubness is a good measure of point centrality within a high-dimensional data cluster, and by proposing several hubness-based clustering algorithms, showing that major hubs can be used effectively as cluster prototypes or as guides during the search for centroid-based cluster configurations. The proposed method called “Neighbor clustering”, which takes as input measures of correspondence between pairs of data points. Experimental results demonstrate good performance of our algorithms in multiple settings.
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Clustering, curse of dimensionality, nearest neighbors, hubs.