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Articles by Singh Vijendra
Total Records ( 2 ) for Singh Vijendra
  Singh Vijendra
  Finding clusters in a high dimensional data space is challenging because a high dimensional data space has hundreds of attributes and hundreds of data tuples and the average density of data points is very low. The distance functions used by many conventional algorithms fail in this scenario. Clustering relies on computing the distance between objects and thus, the complexity of the similarity models has a severe influence on the efficiency of the clustering algorithms. Especially for density-based clustering, range queries must be supported efficiently to reduce the runtime of clustering. The density-based clustering is also influenced by the density divergence problem that affects the accuracy of clustering. If clusters do not exist in the original high dimensional data space, it may be possible that clusters exist in some subspaces of the original data space. Subspace clustering algorithms localize the search for relevant dimensions allowing them to find clusters that exist in multiple, possibly overlapping subspaces. Subspace clustering algorithms identifies such subspace clusters. But for clustering based on relative region densities in the subspaces, density based subspace clustering algorithms are applied where the clusters are regarded as regions whose densities are relatively high as compared to the region densities in a subspace. This study presents a review of various subspaces based clustering algorithms and density based clustering algorithms with their efficiencies on different data sets.
  Singh Vijendra , K. Ashiwini and Sahoo Laxman
  This paper proposed an evolutionary clustering algorithm which can automatically determine the number of clusters present in a data set. The chromosomes are represented as strings of real numbers, encode the centers of a fixed number of clusters. The searching capability of evolutionary clustering is exploited in order to search for appropriate cluster centers in the feature space such that a similarity metric of the resulting clusters is optimized. The proposed clustering approach called Fast Automatic Clustering Evolution (FACE) in data set. To obtain a speedup over linear search in high dimensional data a randomized k-d trees based nearest neighbor search is used. The chromosomes are able to exchange their gene values according to nearest cluster centers and relation among genes in crossover operator. Mutation operator replaced the mutation gene value with respect to nearest neighbor cluster. Adaptive probabilities of crossover and mutation are employed to prevent the convergence of the GA (Genetic Algorithm) to a local optimum. The Adjusted-Rand Index is used as a measure of the validity of the clusters. Effectiveness of the proposed algorithm is demonstrated for both artificial and real-life data sets. The experimental result demonstrates that the proposed clustering algorithm (FACE) has high performance, effectiveness and flexibility. The proposed evolutionary algorithm is able for clustering low to high dimensional data set.
 
 
 
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