Learning functional logic classification concepts from databases

In this paper we address the possibilities, advantages and shortcomings of addressing different data-mining problems with the Inductive Functional Logic Programming (IFLP) paradigm. As a functional extension of the Inductive Logic Programming (ILP) approach, IFLP has all the advantages of the latter but the potential of a more natural representation language for classification, clustering and functional dependencies problems. Two issues are extremely important for successfully tackling these problems: incremental learning to handle large volumes of data and a consistent and flexible classes distribution evaluation to select among many possible hypotheses. We illustrate how these features are included in the IFLP paradigm and show some results with our system FLIP.