European Journal of Molecular & Clinical Medicine

Abstract

Time-Series Analysis exhibitedefficient results in delivering significant knowledge in numerous domains.
Most of the investigationon Time-Series Analysis is restrictedwith the
requirementofexpensivecategorized information. This led tothe growth of curiosity in groupingthe timeseries
informationthat does not need any access to categorized information. The clustering time-series
informationcarries out issues that donot prevail in conventional clustering methodologies.,in the
Euclidean space amongst the objects.Therefore,the authorsuggested an innovativeclustertechnique,
forTime-Seriesemploying of DTW similarity measure by extracting unsupervised shapelets. And these
extracted u-shapelets are clustered employing iterative k-means algorithm. The DTW similarity measure
provides better accuracy in formed clusters of proposed methodology compared tothe Metric
EuclidianDistance Measure. The performance of the suggested approach is evaluated employing theRand
Index (RI) Measure. The experimental for this approach was performed on four different Time-Series
data samples and the outcomes showed that the RI measure for the DTW based Time-Series Clustering
Algorithm is more when compared to the Existing ED-basedTime-Series Clustering Algorithm.