On Multi-Relational Data Mining for Foundation of Data Mining

Abstract

Multi-relational data mining (MRDM) deals with knowledge discovery from relational databases consisting of one or multiple tables. As a typical technique for MRDM, inductive logic programming (ILP) has the power of dealing with reasoning related to various data mining tasks in a "unified" way. Like granular computing (GrC), ILP-based MRDM models the data and the mining process on these data through intension and extension of concepts. Unlike GrC, however, the inference ability of ILP-based MRDM lies in the powerful Prolog-like search engine. Although this important feature suggests that through ILP, MRDM can contribute to the foundation of data mining (FDM), the interesting perspective of "ILP-based MRDM for FDM" has not been investigated in the past. In this paper, we examine this perspective. We provide justification and observations, and report results of related experiments. The primary objective of this paper is to draw attention to FDM researchers from the ILP-based MRDM perspective.