Seqret: Mining Rule Sets from Event Sequences
This addresses the need for more comprehensive summarization of event sequences in data mining, though it appears incremental as it builds on existing pattern discovery by incorporating dependencies.
The paper tackled the problem of discovering both conditional and unconditional dependencies in event sequences, which existing methods often neglect, by proposing the Seqret method to mine rule sets. The result showed that Seqret recovers ground truth on synthetic datasets and finds useful rules in real datasets, unlike state-of-the-art methods.
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.