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.