从事件序列中挖掘条件与非条件依赖规则，提升可解释性
Seqret: Mining Rule Sets from Event Sequences
- 基于最小描述长度原则，挖掘前后件为序列模式的规则
- 在合成与真实数据集上均能准确恢复真实规则并发现有用模式
- 适合需要可解释性强规则的事件分析场景
事件序列总结是数据挖掘的关键环节。现有方法多忽略条件依赖，仅关注序列模式发现。本文研究从事件序列数据中同时发现条件与非条件依赖的问题，通过挖掘形如 $X \rightarrow Y$ 的规则实现，其中 $X$ 与 $Y$ 均为序列模式。此类规则直观易懂，能清晰描述前件与后件间的关系。为获得简洁且无冗余的规则集，我们基于最小描述长度（MDL）原则形式化该问题。由于搜索空间巨大且缺乏结构，我们提出 Seqret 方法，在实践中高效发现高质量规则集。通过大量实证评估，结果表明：相比现有最优方法，Seqret 在合成数据上能准确恢复真实规则，在真实数据上亦能挖掘出有意义的规则。
原文摘要 · Abstract (English)
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.
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