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Similarity Measure Selection for Clustering Time Series Databases

Similarity Measure Selection for Clustering Time Series Databases

Starting at: Rs.4,500.00

4500 reward points

Similarity Measure Selection for Clustering Time Series Databases

 In the past few years, clustering has become a popular task associated with time series. The choice of a suitable distance measure is crucial to the clustering process and, given the vast number of distance measures for time series available in the literature and their diverse characteristics, this selection is not straightforward. With the objective of simplifying this task, we propose a multi-label classification framework that provides the means to automatically select the most suitable distance measures for clustering a time series database. This classifier is based on a novel collection of characteristics that describe the main features of the time series databases and provide the predictive information necessary to discriminate between a set of distance measures. In order to test the validity of this classifier, we conduct a complete set of experiments using both synthetic and real time series databases and a set of 5 common distance measures. The positive results obtained by the designed classification framework for various performance measures indicate that the proposed methodology is useful to simplify the process of distance selection in time series clustering tasks.


 


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  • Model: PROJ5359
  • 999 Units in Stock
  • Manufactured by: ClickMyProjects

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This product was added to our catalog on Saturday 17 September, 2016.

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