Main Reference PaperConstrained distance based clustering for time-series-a comparative and experimental study, Applied Intelligence, 2018 [Java/Python/R]
  • A Euclidean distance metric is not suitable for time series data clustering. To solve this issue, this work proposes a Dynamic Time Warping based distance measure for clustering. k-Means based clustering algorithms are applied to time-series as they iteratively calculate distances to cluster centroids and update these centroids. The cluster centroid point is calculated based on the DTW measure and the DTW Barycenter Averaging algorithm.

+ Description
  • A Euclidean distance metric is not suitable for time series data clustering. To solve this issue, this work proposes a Dynamic Time Warping based distance measure for clustering. k-Means based clustering algorithms are applied to time-series as they iteratively calculate distances to cluster centroids and update these centroids. The cluster centroid point is calculated based on the DTW measure and the DTW Barycenter Averaging algorithm.

  • Reducing the execution time

  • To improve the clustering accuracy.

+ Aim & Objectives
  • Reducing the execution time

  • To improve the clustering accuracy.

  • The percentage of cluster overlapping decreases in the proposed scheme.

+ Contribution
  • The percentage of cluster overlapping decreases in the proposed scheme.

  • OS: Ubuntu 12.04 LTS 64bit

  • Language: Java/Python/R

+ Software Tools & Technologies
  • OS: Ubuntu 12.04 LTS 64bit

  • Language: Java/Python/R

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+ Project Recommended For
  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

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+ Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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