Cluster-Grouping: From Subgroup Discovery to Clustering

Zimmermann, Albrecht; De Raedt, Luc

doi:10.1007/S10994-009-5121-Y

Cluster-Grouping: From Subgroup Discovery to Clustering

Albrecht Zimmermann, Luc De Raedt

MLJ 2009 pp. 125-159

doi:10.1007/S10994-009-5121-Y /mlj/2009/zimmermann2009mlj-clustergrouping/

Abstract

We introduce the problem of cluster-grouping and show that it can be considered a subtask in several important data mining tasks, such as subgroup discovery , mining correlated patterns, clustering and classification . The algorithm CG for solving cluster-grouping problems is then introduced, and it is incorporated as a component in several existing and novel algorithms for tackling subgroup discovery , clustering and classification . The resulting systems are empirically compared to state-of-the-art systems such as CN2, CBA, Ripper, Autoclass and CobWeb. The results indicate that the CG algorithm can be useful as a generic local pattern mining component in a wide variety of data mining and machine learning algorithms.

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Cite

Text

Zimmermann and De Raedt. "Cluster-Grouping: From Subgroup Discovery to Clustering." Machine Learning, 2009. doi:10.1007/S10994-009-5121-Y

Markdown

[Zimmermann and De Raedt. "Cluster-Grouping: From Subgroup Discovery to Clustering." Machine Learning, 2009.](https://mlanthology.org/mlj/2009/zimmermann2009mlj-clustergrouping/) doi:10.1007/S10994-009-5121-Y

BibTeX

@article{zimmermann2009mlj-clustergrouping,
  title     = {{Cluster-Grouping: From Subgroup Discovery to Clustering}},
  author    = {Zimmermann, Albrecht and De Raedt, Luc},
  journal   = {Machine Learning},
  year      = {2009},
  pages     = {125-159},
  doi       = {10.1007/S10994-009-5121-Y},
  volume    = {77},
  url       = {https://mlanthology.org/mlj/2009/zimmermann2009mlj-clustergrouping/}
}