Cluster-Grouping: From Subgroup Discovery to Clustering

Zimmermann, Albrecht; De Raedt, Luc

doi:10.1007/978-3-540-30115-8_56

Cluster-Grouping: From Subgroup Discovery to Clustering

Albrecht Zimmermann, Luc De Raedt

ECML-PKDD 2004 pp. 575-577

doi:10.1007/978-3-540-30115-8_56 /ecmlpkdd/2004/zimmermann2004ecml-clustergrouping/

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 .

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Cite

Text

Zimmermann and De Raedt. "Cluster-Grouping: From Subgroup Discovery to Clustering." European Conference on Machine Learning, 2004. doi:10.1007/978-3-540-30115-8_56

Markdown

[Zimmermann and De Raedt. "Cluster-Grouping: From Subgroup Discovery to Clustering." European Conference on Machine Learning, 2004.](https://mlanthology.org/ecmlpkdd/2004/zimmermann2004ecml-clustergrouping/) doi:10.1007/978-3-540-30115-8_56

BibTeX

@inproceedings{zimmermann2004ecml-clustergrouping,
  title     = {{Cluster-Grouping: From Subgroup Discovery to Clustering}},
  author    = {Zimmermann, Albrecht and De Raedt, Luc},
  booktitle = {European Conference on Machine Learning},
  year      = {2004},
  pages     = {575-577},
  doi       = {10.1007/978-3-540-30115-8_56},
  url       = {https://mlanthology.org/ecmlpkdd/2004/zimmermann2004ecml-clustergrouping/}
}