Integrated Method for Knowledge Discovery in Databases

This paper suggests an integrated method for discovering knowledge from a large database. Our approach applies an attribute-oriented concept hierarchy ascension technique to extract generalized data from actural data in databases, induction of decision trees to measure the value of information, and knowledge reduction of rough set theory to remove dispensable attributes and attribute values. The integrated algorithm first reduce the size of database for the concept generalization, reduces the number of attributes by way of elimination condition attributes which have little influence on decision attribute, and finally induces simplified decision rules removing the dispensable attribute values by analyzing the dependency relationships among the attributes.