Attribute-oriented Induction in Ob Ject-oriented Databases

Knowledge discovery in databases is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data such that the extracted knowledge may facilitate deductive reasoning and query processing in database systems. This branch of study has been ranked among the most promising topics for database research for the 1990s. Due to the dominating influence of relational databases in many application fields, knowledge discovery from databases has been largely focused on relational databases. The gradual adoption of object-oriented database systems has expressed a need for the study of knowledge discovery from object-oriented databases as well. Object-oriented databases(O0DBs) are concerned with complex data structures and diverge greatly from relational database systems. In order to effectively conduct knowledge discovery in an object-oriented database, existing relational algorithms need be modified accordingly to take full advantage of the object-oriented data model. The attribute-oriented induction method has been successful for knowledge discovery in relational databases and we choose this method to study the new demands OODBs impose on a learning algorithm. In this thesis, we study the characteristics of the object-oriented data model and their effects on the attribute-oriented induction algorithm. We extend the attribute-oriented induction method to object-oriented paradigms, focusing on handling complex attributes, and present a algorithm for learning characteristic rules in an object-oriented database. We follow the least commitment principle and break down complex objects into primitive ones and then apply attribute-oriented generalization techniques. Learning in databases with a cycled class composition hierarchy is specifically addressed.