它的长与短：用连续事件概括事件序列
The Long and the Short of It: Summarising Event Sequences with Serial Episodes
摘要：
模式挖掘的一个理想结果是一个信息模式的小集合，不包含冗余或噪声，它识别手头数据的关键结构。标准的频繁模式挖掘器无法实现这一目标，因为由于模式爆炸，通常会返回非常大量的高度冗余模式。我们追求理想的顺序数据，采用模式集挖掘方法的方法，而不是排名模式单独，我们认为结果作为一个整体。模式集挖掘已经成功地应用于事务数据，但令人惊讶的是，对序列数据的研究却很少。在本文中，我们采用的MDL原则，以确定一组序列模式，总结数据最好的。特别是，我们形式化如何编码序列数据使用一组连续的情节，并使用编码的长度作为质量分数。作为搜索策略，我们提出了两种方法：第一种算法从一个大的候选集中选择一个好的模式集，而第二种是一个无参数的任何时间算法，挖掘模式集直接从数据。对合成数据和真实数据的实验表明，我们可以有效地发现小的信息模式集。
英文摘要：
An ideal outcome of pattern mining is a small set of informative patterns, containing no redundancy or noise, that identifies the key structure of the data at hand. Standard frequent pattern miners do not achieve this goal, as due to the pattern explosion typically very large numbers of highly redundant patterns are returned. We pursue the ideal for sequential data, by employing a pattern set mining approach-an approach where, instead of ranking patterns individually, we consider results as a whole. Pattern set mining has been successfully applied to transactional data, but has been surprisingly under studied for sequential data. In this paper, we employ the MDL principle to identify the set of sequential patterns that summarises the data best. In particular, we formalise how to encode sequential data using sets of serial episodes, and use the encoded length as a quality score. As search strategy, we propose two approaches: the first algorithm selects a good pattern set from a large candidate set, while the second is a parameter-free any-time algorithm that mines pattern sets directly from the data. Experimentation on synthetic and real data demonstrates we efficiently discover small sets of informative patterns.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
