ACM Computing Surveys (CSUR)
Self-Organizing Maps
Locally adaptive dimensionality reduction for indexing large time series databases
ACM Transactions on Database Systems (TODS)
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Time series forecasting: Obtaining long term trends with self-organizing maps
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Self organization of a massive document collection
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
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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.