Interesting Multi-Relational Patterns

TL;DR

This paper introduces a novel pattern mining approach for multi-relational data using a new syntax based on connected subgraphs in a K-partite graph, along with an efficient algorithm and interestingness measure, demonstrated on real and synthetic data.

Contribution

It presents a new syntax for multi-relational patterns, an efficient mining algorithm called RMiner, and a method to quantify pattern interestingness, advancing multi-relational data mining.

Findings

RMiner efficiently mines multi-relational patterns.

The pattern syntax generalizes well-known tiles.

Results demonstrate the approach's usefulness on real and synthetic data.

Abstract

Mining patterns from multi-relational data is a problem attracting increasing interest within the data mining community. Traditional data mining approaches are typically developed for highly simplified types of data, such as an attribute-value table or a binary database, such that those methods are not directly applicable to multi-relational data. Nevertheless, multi-relational data is a more truthful and therefore often also a more powerful representation of reality. Mining patterns of a suitably expressive syntax directly from this representation, is thus a research problem of great importance. In this paper we introduce a novel approach to mining patterns in multi-relational data. We propose a new syntax for multi-relational patterns as complete connected subgraphs in a representation of the database as a K-partite graph. We show how this pattern syntax is generally applicable to multirelational data, while it reduces to well-known tiles [7] when the data is a simple binary or attribute-value table. We propose RMiner, an efficient algorithm to mine such patterns, and we introduce a method for quantifying their interestingness when contrasted with prior information of the data miner. Finally, we illustrate the usefulness of our approach by discussing results on real-world and synthetic databases.