Relationship-aware sequential pattern mining

TL;DR

This paper introduces RaSP, a novel algorithm for relationship-aware sequential pattern mining that effectively discovers frequent, hierarchical patterns in complex, taxonomy-labeled sequences, demonstrated on medical treatment data.

Contribution

RaSP is a new two-stage algorithm that efficiently mines relationship-aware patterns with hierarchical taxonomies in sequences, advancing the state of the art.

Findings

RaSP successfully mined medical behavior patterns in antibiotic treatments.

The algorithm demonstrated high computational efficiency.

It effectively handles complex hierarchical relationships in sequences.

Abstract

Relationship-aware sequential pattern mining is the problem of mining frequent patterns in sequences in which the events of a sequence are mutually related by one or more concepts from some respective hierarchical taxonomies, based on the type of the events. Additionally events themselves are also described with a certain number of taxonomical concepts. We present RaSP an algorithm that is able to mine relationship-aware patterns over such sequences; RaSP follows a two stage approach. In the first stage it mines for frequent type patterns and {\em all} their occurrences within the different sequences. In the second stage it performs hierarchical mining where for each frequent type pattern and its occurrences it mines for more specific frequent patterns in the lower levels of the taxonomies. We test RaSP on a real world medical application, that provided the inspiration for its development, in which we mine for frequent patterns of medical behavior in the antibiotic treatment of microbes and show that it has a very good computational performance given the complexity of the relationship-aware sequential pattern mining problem.