具有连续事件的事件序列汇总：一个统计模型及其应用
Summarizing Event Sequences with Serial Episodes: A Statistical Model and an Application
摘要：
在本文中，我们解决的问题，发现一个小的一组频繁的连续事件从序列数据，以便充分表征或总结的数据。我们讨论的算法的基础上的最小描述长度（MDL）的原则和算法是一个轻微的修改早期的方法，称为CSC-2。我们提出了一种新的生成模型的序列数据包含突出的对连续发作，并使用此，提供了一些统计的理由的算法。我们相信这是第一个这样的统计理由的MDL为基础的算法，总结事件序列数据。然后，我们提出了一种新的应用这种数据采掘算法在文本分类。通过将文本文档视为单词的时间序列，数据采掘算法可以为所有训练数据作为一个整体找到一组特征片段。作为这些特征片段的一部分的词然后可以被认为是字典的唯一相关词，从而导致显著降低的特征向量维度。我们通过使用基准数据集的模拟实验表明，发现的频繁发作可以用来实现字典大小减少四倍以上，而不会失去任何分类精度。
英文摘要：
In this paper we address the problem of discovering a small set of frequent serial episodes from sequential data so as to adequately characterize or summarize the data. We discuss an algorithm based on the Minimum Description Length (MDL) principle and the algorithm is a slight modification of an earlier method, called CSC-2. We present a novel generative model for sequence data containing prominent pairs of serial episodes and, using this, provide some statistical justification for the algorithm. We believe this is the first instance of such a statistical justification for an MDL based algorithm for summarizing event sequence data. We then present a novel application of this data mining algorithm in text classification. By considering text documents as temporal sequences of words, the data mining algorithm can find a set of characteristic episodes for all the training data as a whole. The words that are part of these characteristic episodes could then be considered the only relevant words for the dictionary thus resulting in a considerably reduced feature vector dimension. We show, through simulation experiments using benchmark data sets, that the discovered frequent episodes can be used to achieve more than four-fold reduction in dictionary size without losing any classification accuracy.
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。
