Clustering time series for automatic similarity measurement selection of Database
Keywords:
Multi-label classification framework, SOM Clustering, K-Means, Clustering, Time Series databaseAbstract
Clustering has turned into a famous undertaking related with time series. The decision of an appropriate measurement of distance is pivotal of the clustered system and, the immense number of measurable distance of time series accessible in the writing and their different attributes, this choice isn't clear. With the target of working on this errand, we propose a multi-name arrangement structure that gives the resources to consequently choose the most reasonable measurable distance of cluster: a period series data set. This is classified depends on an original assortment of attributes that depict the fundamental elements of the time series data sets and give the prescient data important to separate between clusters measurement of distance. To test the legitimacy of this classifier, we direct a total arrangement of investigations utilizing both engineered and constant series data sets and a cluster of 5 normal distance measures. The positive outcomes got by the planned grouping structure for different execution measures show that, the proposed theory is helpful to improve on the course of distance choice in time series clustering undertakings.
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Copyright (c) 2022 M Thurai Pandian, P Damodharan, K R Bhavya, Sanjay Singh, K Anitha, Ankur Kumar Aggarwal
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