A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy

By Nurjahan Begum et al

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Table of Contents

1. Introduction
2. Related Work
3. Problem Overview
4. Proposed Algorithm
5. Experimental Results
6. Conclusion

Summary

Time Series Clustering is an important subroutine in many higher-level data mining analyses, including data editing for classifiers, summarization, and outlier detection. Clustering time series under Dynamic Time Warping (DTW) remains a computationally expensive operation. In this work, a novel pruning strategy that exploits both upper and lower bounds to prune off a very large fraction of the expensive distance calculations is proposed. The pruning strategy is admissible and gives provably identical results to the brute force algorithm but is significantly faster. The utility of the approach is demonstrated with case studies in various domains. The proposed algorithm goes beyond existing literature by showing that calculating the full distance matrix is unnecessary in the general case, thanks to the exploitation of upper and lower bounds to DTW.