An FCA approach to mining quantitative association rules from multi-relational data

Abstract

In this paper, we propose an algorithm for mining quantitative association rules (ARs) from a multi-relational database (MRDB). A MRDB contains multiple tables (relations), and attributes in a table are either categorical or quantitative (or numerical). To handle numerical data in a pattern, we consider (logical) conjunctions with interval constraints, using the notion of closed interval patterns (CIPs) proposed by Kaytoue et al. in formal concept analysis (FCA). We then present an algorithm for mining strong quantitative ARs, namely they satisfy both a minimum support and a minimum confidence. We also propose a pruning method tailored to computing CIPs in an AR. We give some experimental results, which show the effectiveness of the proposed method, compared with the conventional methods such as a discretisation-based approach or an optimisation-based approach.