Fast Randomized Algorithms for Robust Estimation of Location.
TSDM '00: Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers(2000)
摘要
A fundamental procedure appearing within such clustering methods as k -Means, Expectation Maximization, Fuzzy-C-Means and Minimum Message Length is that of computing estimators of location. Most estimators of location exhibiting useful robustness properties require at least quadratic time to compute, far too slow for large data mining applications. In this paper, we propose O ( Dn √ n )-time randomized algorithms for computing robust estimators of location, where n is the size of the data set, and D is the dimension.
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关键词
data set,large data mining application,quadratic time,time randomized algorithm,Expectation Maximization,Minimum Message Length,clustering method,fundamental procedure,robust estimator,useful robustness property,Fast Randomized Algorithms,Robust Estimation
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