Discovering Useful Compact Sets of Sequential Rules in a Long Sequence

TL;DR

This paper introduces COSSU, an algorithm that efficiently mines small, meaningful sets of sequential rules from long sequences, providing interpretable models with competitive predictive accuracy.

Contribution

COSSU employs an MDL-inspired criterion and a novel encoding scheme to extract compact, relevant sequential rules, advancing sequence modeling techniques.

Findings

COSSU successfully retrieves relevant closed sequential rules.

The rules enable interpretable models for prediction and classification.

COSSU achieves competitive accuracy compared to existing methods.

Abstract

We are interested in understanding the underlying generation process for long sequences of symbolic events. To do so, we propose COSSU, an algorithm to mine small and meaningful sets of sequential rules. The rules are selected using an MDL-inspired criterion that favors compactness and relies on a novel rule-based encoding scheme for sequences. Our evaluation shows that COSSU can successfully retrieve relevant sets of closed sequential rules from a long sequence. Such rules constitute an interpretable model that exhibits competitive accuracy for the tasks of next-element prediction and classification.