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A distributed framework for trimmed Kernel k-Means clustering
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DOI：10.1016/j.patcog.2015.02.020.png)
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摘要
En 中文
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. Kernel k-Means is a state of the art clustering algorithm. However, in contrast to clustering algorithms that can work using only a limited percentage of the data at a time, Kernel k-Means is a global clustering algorithm. It requires the computation of the kernel matrix, which takes O(n(2)d) time and O(n(2)) space in memory. As datasets grow larger, the application of Kernel k-Means becomes infeasible on a single computer, a fact that strongly suggests a distributed approach. In this paper, we present such an approach to the Kernel k-Means clustering algorithm, in order to make its application to a large number of samples feasible and, thus, achieve high performance clustering results on very big datasets. Our distributed approach follows the MapReduce programming model and consists of 3 stages, the kernel matrix computation, a novel kernel matrix trimming method and the Kernel k-Means clustering algorithm. (C) 2015 Elsevier Ltd. All rights reserved.
Keyword:
Data clustering
Face clustering
Kernel k-Means
Distributed computing
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IF:
7.6
论文数:
1.3W
被引数:
4.5W
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