Deriving quantitative models for correlation clusters

Correlation clustering aims at grouping the data set into correlation clusters such that the objects in the same cluster exhibit a certain density and are all associated to a common arbitrarily oriented hyperplane of arbitrary dimensionality. Several algorithms for this task have been proposed recently. However, all algorithms only compute the partitioning of the data into clusters. This is only a first step in the pipeline of advanced data analysis and system modelling. The second (post-clustering) step of deriving a quantitative model for each correlation cluster has not been addressed so far. In this paper, we describe an original approach to handle this second step. We introduce a general method that can extract quantitative information on the linear dependencies within a correlation clustering. Our concepts are independent of the clustering model and can thus be applied as a post-processing step to any correlation clustering algorithm. Furthermore, we show how these quantitative ...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Arthur Zimek, Christian Böhm, Elke Achtert, Hans-Peter Kriegel, Peer Kröger
• Keywords: Correlation Clustering, Correlation Clustering Algorithm, Correlation Clusters, Data Mining, KDD 2006