Abstract
The problem of cluster-grouping is defined. It integrates subgroup discovery, mining correlated patterns and aspects from clustering. The algorithm CG for solving cluster-grouping problems is presented and experimentally evaluated on a number of real-life data sets. The results indicate that the algorithm improves upon the subgroup discovery algorithm CN2-WRACC and is competitive with the clustering algorithm CobWeb. © Springer-Verlag Berlin Heidelberg 2004.
Author supplied keywords
Cite
CITATION STYLE
Zimmermann, A., & De Raedt, L. (2004). Cluster-grouping: From subgroup discovery to clustering. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3201, pp. 575–577). Springer Verlag. https://doi.org/10.1007/978-3-540-30115-8_56
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
