Seqret: Mining Rule Sets from Event Sequences

TL;DR

Seqret is a novel data mining method that discovers both conditional and unconditional rule dependencies in event sequences, effectively summarizing complex temporal data with high accuracy.

Contribution

The paper introduces Seqret, a new approach for mining succinct rule sets from event sequences using the Minimum Description Length principle, addressing limitations of existing methods.

Findings

Seqret accurately recovers ground truth on synthetic data.

It finds meaningful rules in real-world datasets.

Outperforms state-of-the-art methods in rule discovery.

Abstract

Summarizing event sequences is a key aspect of data mining. Most existing methods neglect conditional dependencies and focus on discovering sequential patterns only. In this paper, we study the problem of discovering both conditional and unconditional dependencies from event sequence data. We do so by discovering rules of the form $X \rightarrow Y$ where $X$ and $Y$ are sequential patterns. Rules like these are simple to understand and provide a clear description of the relation between the antecedent and the consequent. To discover succinct and non-redundant sets of rules we formalize the problem in terms of the Minimum Description Length principle. As the search space is enormous and does not exhibit helpful structure, we propose the Seqret method to discover high-quality rule sets in practice. Through extensive empirical evaluation we show that unlike the state of the art, Seqret ably recovers the ground truth on synthetic datasets and finds useful rules from real datasets.