Pattern theoretic knowledge discovery

Abstract

Future research directions in knowledge discovery in databases (KDD) include the ability to extract an overlying concept relating useful data. Current limitations involve the search complexity to find that concept and what it means to be "useful." The pattern theory research crosses over in a natural way to the aforementioned domain. The goal of this paper is threefold. First, we present a new approach to the problem of learning by discovery and robust pattern finding. Second, we explore the current limitations of a pattern theoretic approach as applied to the general KDD problem. Third, we exhibit its performance with experimental results on binary functions, and we compare those results with C4.5. This new approach to learning demonstrates a powerful method for finding patterns in a robust manner.<>