A Simple Linear Time (1+ ∊) -Approximation Algorithm for k-Means Clustering in Any Dimensions 论文

2004引用 248
DOI
Data Management and AlgorithmsAdvanced Clustering Algorithms ResearchAlgorithms and Data Compression
Algorithms and Data CompressionAdvanced Clustering Algorithms ResearchData Management and Algorithms
相关技术:Algorithms and Data CompressionData Management and AlgorithmsAdvanced Clustering Algorithms Research
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摘要

We present the first linear time (1 + /spl epsiv/)-approximation algorithm for the k-means problem for fixed k and /spl epsiv/. Our algorithm runs in O(nd) time, which is linear in the size of the input. Another feature of our algorithm is its simplicity - the only technique involved is random sampling.

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Subhankar Sen

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Yogish Sabharwal

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Algorithms and Data CompressionData Management and AlgorithmsAdvanced Clustering Algorithms Research

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