Previous Distance Density Clustering has shown some promising results for univariate time series datasets. However, due to the nature of time series data and from using Dynamic Time Warping algorithm as the distance measure; Distance Density Clustering is not an efficient heuristic with larger datasets. In this paper we propose a preprocessing step that could augment the algorithm to the parallel case, and speed up the Distance Density Clustering process considerably. We use time series sequence feature: peak numbers, to prune impossible match-ings. By doing so, we are able to form preliminary feature clusters, and further clustering is applied within each feature cluster individually. This can narrow down the amount of time series distance computations, and make Distance Density Clustering scalable.
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