Hybrid ASP-based Approach to Pattern Mining

TL;DR

This paper introduces a hybrid ASP-based framework that combines specialized mining algorithms with declarative ASP filtering to efficiently detect relevant patterns across itemsets, sequences, graphs, and approximate tiling, enhancing flexibility and performance.

Contribution

It presents a novel hybrid approach integrating optimized pattern mining systems with ASP-based filtering, enabling generic and efficient pattern detection across multiple data types.

Findings

Effective pattern detection demonstrated on real datasets.

Significant computational gains over traditional methods.

Versatile application to itemsets, sequences, graphs, and tiling.

Abstract

Detecting small sets of relevant patterns from a given dataset is a central challenge in data mining. The relevance of a pattern is based on user-provided criteria; typically, all patterns that satisfy certain criteria are considered relevant. Rule-based languages like Answer Set Programming (ASP) seem well-suited for specifying such criteria in a form of constraints. Although progress has been made, on the one hand, on solving individual mining problems and, on the other hand, developing generic mining systems, the existing methods either focus on scalability or on generality. In this paper we make steps towards combining local (frequency, size, cost) and global (various condensed representations like maximal, closed, skyline) constraints in a generic and efficient way. We present a hybrid approach for itemset, sequence and graph mining which exploits dedicated highly optimized mining systems to detect frequent patterns and then filters the results using declarative ASP. To further demonstrate the generic nature of our hybrid framework we apply it to a problem of approximately tiling a database. Experiments on real-world datasets show the effectiveness of the proposed method and computational gains for itemset, sequence and graph mining, as well as approximate tiling. Under consideration in Theory and Practice of Logic Programming (TPLP).