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The normal cloud model can be applied to set similarity thresholds adaptively without domain knowledge. Then some distances that are not similar because of noise or other reasons are filtered out. Next, the time series are mapped into a complex network to represent the similarity between time series fragments.
A time-series clustering method is proposed based on the normal cloud model and complex networks. Matrix profile similarity measurement, normal cloud model ...
The forward normal cloud generator is an algorithm that generates cloud droplets conforming to a normal distribution, producing the expected quantity of ...
Time series clustering based on normal cloud model and complex network. https://doi.org/10.1016/j.asoc.2023.110876 ·. Journal: Applied Soft Computing, 2023, p ...
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Time series clustering based on normal cloud model and complex network. Appl. Soft Comput. Pub Date : 2023-10-04. DOI : 10.1016/j.asoc.2023.110876.
Jan 1, 2023 · This paper proposes a time series Clustering method based on Synchronous matching of Complex networks (CSC). This method uses density peak ...
Techniques such as clustering can extract valuable information and potential patterns from time-series data. In this regard, the clustering analysis of ...
In this paper we propose a clustering approach, based on complex network analysis, for the unsupervised FSS of time series in sensor networks.
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Time series clustering based on normal cloud model and complex network. Applied Soft Computing,2023. Hits:20. Translation or Not:no. Pre One:林春培, 朱晓艳 ...
Clustering is a solution for classifying enormous data when there is not any early knowledge about classes. With emerging new concepts like cloud computing ...