Computing Clusters of Correlation Connected Objects

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Computing Clusters of Correlation Connected Objects
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...
Christian Böhm, Karin Kailing, Peer Krög
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2004
Authors Christian Böhm, Karin Kailing, Peer Kröger, Arthur Zimek
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