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  1. Knowledge and Information Systems
  2. Knowledge and Information Systems : Volume 14
  3. Knowledge and Information Systems : Volume 14, Issue 3, March 2008
  4. Robust projected clustering
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Knowledge and Information Systems : Volume 51
Knowledge and Information Systems : Volume 50
Knowledge and Information Systems : Volume 49
Knowledge and Information Systems : Volume 48
Knowledge and Information Systems : Volume 47
Knowledge and Information Systems : Volume 46
Knowledge and Information Systems : Volume 45
Knowledge and Information Systems : Volume 44
Knowledge and Information Systems : Volume 43
Knowledge and Information Systems : Volume 42
Knowledge and Information Systems : Volume 41
Knowledge and Information Systems : Volume 40
Knowledge and Information Systems : Volume 39
Knowledge and Information Systems : Volume 38
Knowledge and Information Systems : Volume 37
Knowledge and Information Systems : Volume 36
Knowledge and Information Systems : Volume 35
Knowledge and Information Systems : Volume 34
Knowledge and Information Systems : Volume 33
Knowledge and Information Systems : Volume 32
Knowledge and Information Systems : Volume 31
Knowledge and Information Systems : Volume 30
Knowledge and Information Systems : Volume 29
Knowledge and Information Systems : Volume 28
Knowledge and Information Systems : Volume 27
Knowledge and Information Systems : Volume 26
Knowledge and Information Systems : Volume 25
Knowledge and Information Systems : Volume 24
Knowledge and Information Systems : Volume 23
Knowledge and Information Systems : Volume 22
Knowledge and Information Systems : Volume 21
Knowledge and Information Systems : Volume 20
Knowledge and Information Systems : Volume 19
Knowledge and Information Systems : Volume 18
Knowledge and Information Systems : Volume 17
Knowledge and Information Systems : Volume 16
Knowledge and Information Systems : Volume 15
Knowledge and Information Systems : Volume 14
Knowledge and Information Systems : Volume 14, Issue 3, March 2008
Ensembles of relational classifiers
Robust projected clustering
Forecasting skewed biased stochastic ozone days: analyses, solutions and beyond
Random walk with restart: fast solutions and applications
Comparison of descriptor spaces for chemical compound retrieval and classification
The importance of generalizability for anomaly detection
Knowledge and Information Systems : Volume 14, Issue 2, February 2008
Knowledge and Information Systems : Volume 14, Issue 1, January 2008
Knowledge and Information Systems : Volume 13
Knowledge and Information Systems : Volume 12
Knowledge and Information Systems : Volume 11
Knowledge and Information Systems : Volume 10
Knowledge and Information Systems : Volume 9
Knowledge and Information Systems : Volume 8
Knowledge and Information Systems : Volume 7
Knowledge and Information Systems : Volume 6
Knowledge and Information Systems : Volume 5
Knowledge and Information Systems : Volume 4
Knowledge and Information Systems : Volume 3
Knowledge and Information Systems : Volume 2
Knowledge and Information Systems : Volume 1

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Robust projected clustering

Content Provider Springer Nature Link
Author Moise, Gabriela Sander, Jörg Ester, Martin
Copyright Year 2007
Abstract Projected clustering partitions a data set into several disjoint clusters, plus outliers, so that each cluster exists in a subspace. Subspace clustering enumerates clusters of objects in all subspaces of a data set, and it tends to produce many overlapping clusters. Such algorithms have been extensively studied for numerical data, but only a few have been proposed for categorical data. Typical drawbacks of existing projected and subspace clustering algorithms for numerical or categorical data are that they rely on parameters whose appropriate values are difficult to set appropriately or that they are unable to identify projected clusters with few relevant attributes. We present P3C, a robust algorithm for projected clustering that can effectively discover projected clusters in the data while minimizing the number of required parameters. P3C does not need the number of projected clusters as input, and can discover, under very general conditions, the true number of projected clusters. P3C is effective in detecting very low-dimensional projected clusters embedded in high dimensional spaces. P3C positions itself between projected and subspace clustering in that it can compute both disjoint or overlapping clusters. P3C is the first projected clustering algorithm for both numerical and categorical data.
Starting Page 273
Ending Page 298
Page Count 26
File Format PDF
ISSN 02191377
Journal Knowledge and Information Systems
Volume Number 14
Issue Number 3
e-ISSN 02193116
Language English
Publisher Springer-Verlag
Publisher Date 2007-07-21
Publisher Place London
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Projected clustering Subspace clustering Clustering numerical and categorical data Business Information Systems Information Systems and Communication Service
Content Type Text
Resource Type Article
Subject Artificial Intelligence Information Systems Human-Computer Interaction Hardware and Architecture Software
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