A fuzzy-based heuristic measure evaluating quality of a concept partition: application to SAINTETIQ, a database summarization system

We present an original fuzzy-based heuristic measure, the partition quality, highly integrated to a general database summarization framework. Our system SAINTETIQ is based on an incremental conceptual clustering algorithm, building a concept hierarchy from database observations. Concepts of the hierarchy are summaries of a part of the database, at different levels of abstraction. The algorithm roughly consists in applying learning operators on the hierarchy as new observations arrive into the system. The decision function named partition quality that we introduce, reflects contrast and typicity of a concept partition and allows the system to select the best operator to apply during the construction of the concept hierarchy