![]() ![]() The experimental results on various datasets show that BCNC is superior to traditional multivariate time series clustering methods. Time-series clustering methods are examined in three main sections: data representation, similarity measure, and clustering algorithm. The detailed algorithm and the simulation experiments of the proposed BCNC method are reported. The solution is innovatively based on a relationship network and relies on the use of community detection technology to achieve complete multivariate time series clustering. BCNC includes a new method for mapping multivariate time series into complex networks and a new method to visualize multivariate time series. A clustering algorithm that will perform clustering on each of a time-series of discrete datasets, and explicitly track the evolution of clusters over time. ![]() Our study led us to develop a novel method based on complex networks for multivariate time series clustering (BCNC). Clustering time-series data has been used in diverse scientific areas to discover patterns which empower data analysts to extract valuable information from. In this regard, the clustering analysis of multivariate time series is challenging because of the high dimensionality. Goal of that type of algorithms is discretization. Techniques such as clustering can extract valuable information and potential patterns from time-series data. Other type of time series clustering is presented in partition methods such SAX algorithm (Jessica Lin, 2007). Recent years have seen an increase in research on time series data mining (especially time-series clustering) owing to the widespread existence of time series in various fields. ![]()
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