NDLI: Computing Clusters of Correlation Connected objects

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Computing Clusters of Correlation Connected objects

Content Provider	ACM Digital Library
Author	Kailing, Karin Kröger, Peer Zimek, Arthur Böhm, Christian
Abstract	The detection of correlations between different features in a set of feature vectors is a very important data mining task because correlation indicates a dependency between the features or some association of cause and effect between them. This association can be arbitrarily complex, i.e. one or more features might be dependent from a combination of several other features. Well-known methods like the principal components analysis (PCA) can perfectly find correlations which are global, linear, not hidden in a set of noise vectors, and uniform, i.e. the same type of correlation is exhibited in all feature vectors. In many applications such as medical diagnosis, molecular biology, time sequences, or electronic commerce, however, correlations are not global since the dependency between features can be different in different subgroups of the set. In this paper, we propose a method called 4C (Computing Correlation Connected Clusters) to identify local subgroups of the data objects sharing a uniform but arbitrarily complex correlation. Our algorithm is based on a combination of PCA and density-based clustering (DBSCAN). Our method has a determinate result and is robust against noise. A broad comparative evaluation demonstrates the superior performance of 4C over competing methods such as DBSCAN, CLIQUE and ORCLUS.
Starting Page	455
Ending Page	466
Page Count	12
File Format	PDF
ISBN	1581138598
DOI	10.1145/1007568.1007620
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-06-13
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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