A Multistrategy Learning Approach to Flexible Knowledge Organization and Discovery

Seok Won Lee, Scott Fischthal, and Janusz Wnek

Properly organizing knowledge so that it can be managed often requires the acquisition of patterns and relations from large, distributed, heterogeneous databases. The employment of an intelligent and automated KDD (Knowledge Discovery in Databases) process plays a major role in solving this problem. An integration of diverse learning strategies that cooperatively performs a variety of techniques and develops high quality knowledge can be a productive methodology for addressing this process. AqBC is a multistrategy knowledge discovery approach that combines supervised inductive learning and unsupervised Bayesian classification. This study investigates reorganizing the knowledge with the aid of an unsupervised Bayesian classification system, AutoClass. AutoClass discovers interesting taxonomies from databases. These taxonomies can also be represented as new attributes via constructive induction. The robust inductive learning system, AQ15c, can then learn useful concept relationships between knowledge objects. AqBC applied to two different sample problems yields not only simple but also meaningful knowledge due to the systems that implement its parent approaches.

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