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H-D and Subspace Clustering of Paradoxical High Dimensional Clinical Datasets with Dimension Reduction Techniques – a Model
2016
Indian Journal of Science and Technology
Objectives: Heterogeneous High dimensional data clustering is the analysis of data with multiple dimensions. Large dimensions are not easy to handle. The complexity increases exponentially with the dimensionality. Dimensionality reduction is the conversion of high dimensional data into a considerable representation of reduced dimensionality that corresponds to the essential dimensionality of the data. To solve the problem we put forward a general framework for clustering high dimensional
doi:10.17485/ijst/2016/v9i38/101792
fatcat:m5nnpqdfbrcx3nd4rx37ngrkhu