Martin Atzmueller, Frank Puppe
This paper presents a methodological approach for the semi-automatic refinement and assessment of subgroup patterns using summarization and clustering techniques in the context of intelligent data mining systems. The method provides the suppression of irrelevant (and redundant) patterns and facilitates the intelligent refinement of groups of similar patterns. Furthermore, the presented approach features intuitive visual representations of the relations between the patterns, and appropriate techniques for their inspection and evaluation.
Subjects: 12. Machine Learning and Discovery; 9. Foundational Issues
Submitted: Feb 25, 2008