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Distributed, MapReduce-Based Nearest Neighbor and E-Ball Kernel k-Means
2015
2015 IEEE Symposium Series on Computational Intelligence
Data clustering is an unsupervised learning task that has found many applications in various scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a set of unlabeled data. A classic clustering algorithm is the so-called k-Means. It is very popular, however, it is also unable to handle cases in which the clusters are not linearly separable. Kernel k-Means is a state of the art clustering algorithm, which employs the kernel trick, in order to perform
doi:10.1109/ssci.2015.81
dblp:conf/ssci/TsapanosTNP15
fatcat:djn6hiv2xnazjfngdrji26g7mm