Clustering data streams

被引：278

作者：

Guha, S ^{[1
]}

Mishra, N ^{[1
]}

Motwani, R ^{[1
]}

O'Callaghan, L ^{[1
]}

机构：

[1] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA

来源：

41ST ANNUAL SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE, PROCEEDINGS | 2000年

关键词：

D O I：

10.1109/SFCS.2000.892124

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We study clustering under the data stream model of computation where: given a sequence of points, the objective is to maintain a consistently good clustering of the sequence observed so far, using a small amount of: memory and time. The data stream model is relevant to new classes of applications involving massive data sets, such as web click stream analysis and multimedia data analysis. We give constant-factor approximation algorithms for the Ic-Median problem in the data stream model of computation in a single pass. We also show negative results implying that our algorithms cannot be improved in a certain sense.

引用

页码：359 / 366

页数：8

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