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Pattern Mining for Time Series Based on Cloud Theory Pan-concept-tree

  • Yingjun Weng
  • Zhongying Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3066)

Abstract

One important series mining problems is finding important patterns in larger time series sets. Two limitations of previous works were the poor scalability and the robustness to noise. Here we introduce a algorithm using symbolic mapping based on concept tree. The slope of subsequence is chosen to describe series data. Then, the numerical data is transformed into low dimension symbol by cloud models. Due to characteristic of the cloud models, the loss of data in the course of linear preprocessing is treated. Moreover, it is more flexible for the local noise. Second, cloud Boolean calculation is realized to automatically produce the basic concepts as the leaf nodes in pan-concept-tree which leads to hierarchal discovering of the knowledge .Last, the probabilistic project algorithm was adapted so that comparison among symbols may be carried out with less CPU computing time. Experiments show strong robustness and less time and space complexity.

Keywords

Pattern Mining Dynamic Time Warping Cloud Model Concept Hierarchy Matching Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Yingjun Weng
    • 1
  • Zhongying Zhu
    • 1
  1. 1.Department of AutomationShanghai Jiaotong UniversityShanghaiChina

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