Pattern Discovery from Time Series Using Growing Hierarchical Self-Organizing Map

Abstract

AbstractPattern 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.KeywordsTime Series DataFailure DetectionPattern DiscoveryMultivariate Time SeriesHierarchical RelationThese 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.