NDLI: Density biased sampling:an improved method for data mining and clustering

Content Provider	ACM Digital Library
Author	Palmer, Christopher R. Faloutsos, Christos
Abstract	Data mining in large data sets often requires a sampling or summarization step to form an in-core representation of the data that can be processed more efficiently. Uniform random sampling is frequently used in practice and also frequently criticized because it will miss small clusters. Many natural phenomena are known to follow Zipf's distribution and the inability of uniform sampling to find small clusters is of practical concern. Density Biased Sampling is proposed to probabilistically under-sample dense regions and over-sample light regions. A weighted sample is used to preserve the densities of the original data. Density biased sampling naturally includes uniform sampling as a special case. A memory efficient algorithm is proposed that approximates density biased sampling using only a single scan of the data. We empirically evaluate density biased sampling using synthetic data sets that exhibit varying cluster size distributions finding up to a factor of six improvement over uniform sampling.
Starting Page	82
Ending Page	92
Page Count	11
File Format	PDF
ISSN	01635808
DOI	10.1145/335191.335384
Journal	ACM SIGMOD Record (SGMD)
Volume Number	29
Issue Number	2
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1999-06-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article
Subject	Information Systems Software

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Density biased sampling:an improved method for data mining and clustering