Clustering data streams

Author

Guha, Sudipto ; Mishra, Nina ; Motwani, Rajeev ; O´Callaghan, Liadan

Author_Institution

Dept. of Comput. Sci., Stanford Univ., CA, USA

fYear

2000

fDate

2000

Firstpage

359

Lastpage

366

Abstract

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 k-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

Keywords

computational complexity; data analysis; deterministic algorithms; pattern clustering; very large databases; Web click stream analysis; constant-factor approximation algorithms; data stream clustering; data stream model; deterministic algorithms; k-median problem; massive data sets; multimedia data analysis; point sequence; Application software; Approximation algorithms; Clustering algorithms; Computational modeling; Computer science; Data analysis; Laboratories; Streaming media; Telephony; Web pages;

fLanguage

English

Publisher

ieee

Conference_Titel

Foundations of Computer Science, 2000. Proceedings. 41st Annual Symposium on

Conference_Location

Redondo Beach, CA

ISSN

0272-5428

Print_ISBN

0-7695-0850-2

Type

conf

DOI

10.1109/SFCS.2000.892124

Filename

892124