NDLI: A Framework for Clustering Massive-Domain Data Streams

Content Provider	IEEE Xplore Digital Library
Author	Aggarwal, C.C.
Copyright Year	2009
Abstract	In this paper, we will examine the problem of clustering massive domain data streams. Massive-domain data streams are those in which the number of possible domain values for each attribute are very large and cannot be easily tracked for clustering purposes. Some examples of such streams include IP-address streams, credit-card transaction streams, or streams of sales data over large numbers of items. In such cases, it is well known that even simple stream operations such as counting can be extremely difficult because ofthe difficulty in maintaining summary information over the different discrete values. The task of clustering is significantly more challenging in such cases, since the intermediate statistics for the different clusters cannot be maintained efficiently. In this paper, we propose a method for clustering massive-domain data streams with the use of sketches. We prove probabilistic results which show that a sketch-based clustering method can provide similar results to an infinite-space clustering algorithm with high probability. We present experimental results which validate these theoretical results, and show that it is possible to approximate the behavior of an infinite-space algorithm accurately.
Starting Page	102
Ending Page	113
File Size	327378
Page Count	12
File Format	PDF
ISBN	9781424434220
ISSN	10844627
DOI	10.1109/ICDE.2009.13
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Statistics Clustering methods Probability Data engineering USA Councils Marketing and sales Computational efficiency Design methodology Data structures massive-domain clustering data streams
Content Type	Text
Resource Type	Article
Subject	Signal Processing Information Systems Software

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