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2009
Springer

Incremental Unsupervised Time Series Analysis Using Merge Growing Neural Gas

10 years 1 months ago
Incremental Unsupervised Time Series Analysis Using Merge Growing Neural Gas
We propose Merge Growing Neural Gas (MGNG) as a novel unsupervised growing neural network for time series analysis. MGNG combines the state-of-the-art recursive temporal context of Merge Neural Gas (MNG) with the incremental Growing Neural Gas (GNG) and enables thereby the analysis of unbounded and possibly infinite time series in an online manner. There is no need to define the number of neurons a priori and only constant parameters are used. MGNG utilizes a rather unknown entropy maximization strategy to control the creation of new neurons in order to focus on frequent sequence patterns. Experimental results demonstrate reduced time complexity compared to MNG while retaining similar accuracy in time series representation. Key words: time series analysis, unsupervised, self-organizing, incremental, recursive temporal context
Andreas Andreakis, Nicolai von Hoyningen-Huene, Mi
Added 25 May 2010
Updated 25 May 2010
Type Conference
Year 2009
Where WSOM
Authors Andreas Andreakis, Nicolai von Hoyningen-Huene, Michael Beetz
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