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首页> 外文会议>Neural Information Processing pt.1; Lecture Notes in Computer Science; 4232 >Pattern Discovery from Time Series Using Growing Hierarchical Self-Organizing Map
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Pattern Discovery from Time Series Using Growing Hierarchical Self-Organizing Map

机译：使用增长的分层自组织图从时间序列中发现模式

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摘要

Pattern discovery from time series is an important task in many applications. The unsupervised self-organizing map (SOM) has been widely used in data mining as well as in time series knowledge discovery. However, the traditional SOM has two main limitations: the static architecture and the lacking ability for the representing of hierarchical relations of the data. To overcome these limitations the growing hierarchical self-organizing map (GHSOM) is used to analyze time series in this paper. The experiments conducted on several data sets confirm that the GHSOM can form an adaptive architecture, which grows in size and depth during its training process, thus to unfold the hierarchical structure of the analyzed time series data. It is expected that this method will be effective and efficient to implement and will provide a useful practical tool for pattern discovery from large time series databases.
机译：时间序列中的模式发现是许多应用程序中的重要任务。无监督的自组织映射（SOM）已被广泛用于数据挖掘以及时间序列知识发现中。但是，传统的SOM有两个主要局限性：静态体系结构和缺乏表示数据层次关系的能力。为了克服这些限制，本文使用增长的层次自组织图（GHSOM）来分析时间序列。在几个数据集上进行的实验证实，GHSOM可以形成一种自适应体系结构，该体系结构在训练过程中规模和深度都会增加，从而展开了所分析时间序列数据的层次结构。预期该方法将是有效且高效的实施方法，并将为从大型时间序列数据库中发现模式提供有用的实用工具。

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