情報量と頻度に基づく系列データマイニングにおける非同期パターンの抽出と効率化

抄録

In this paper, we propose a method that extracts asynchronous patterns from a large sequential data based on a frequency and self-information. Sequential data mining based on frequency has been studied widely, but these methods are not always useful. In several application, a pattern of high frequency is often handled just as meaningless noise. We restrain the noisy patterns by using not only frequency but also self-information. This method is based on the study by Yang et.al. , which purposed to extract well-balanced patterns from the view-point of both frequency and self-information. Additionally, We introduce the sliding window enables to extract asynchronous patterns. In order to make calculation e cient, we introduce more elaborate pruning methods and reorganize search space. Empirical tests demonstrates the e ciency and the usefulness of the proposed method.