Inductive Querying for Discovering Subgroups and Clusters

We introduce the problem of cluster-grouping and show that it integrates several important data mining tasks, i.e. subgroup discovery, mining correlated patterns and aspects from clustering. The problem of cluster-grouping can be regarded as a new type of inductive optimization query that asks for the k best patterns according to a convex criterion. The algorithm CG for solving cluster-grouping problems is presented and the underlying mechanisms are discussed. The approach is 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.

• Type: Conference
• Year: 2004
• Where: CINQ
• Authors: Albrecht Zimmermann, Luc De Raedt
• Keywords: Algorithm, CINQ 2004, Database, Subgroup Discovery, Subgroup Discovery Algorithm