Cluster-grouping: From subgroup discovery to clustering

5Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.

This article is free to access.

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.

Cite

CITATION STYLE

APA

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.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free