Robust New Distance Kernelized Approach to Distributed Clustering

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Provided by: IJESAT (International Journal of Engineering Science & Advanced Technology)
Topic: Big Data
Format: PDF
Clustering has become one of the most widely used tasks in analyzing the vast amount of data. In clustering the given datasets are grouped in similar sets where the data points of one group are dissimilar to the data points belonging to other groups. Fuzzy clustering is based on the clustering process which forms the soft clusters. The clustering algorithm DKFCM- new identifies outliers by using density of points in the data-set before creating clusters. It is a density oriented kernelized technique to fuzzy c-means algorithm based on new distance measure.
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