Density-based clustering of time series subsequences
Anne M Denton · 2004
Doubts have been raised that time series subsequences can be clustered in a meaningful way. This paper introduces a kernel-density-based algorithm that detects meaningful patterns in the presence of a vast number of random-walk-like subsequences. The value of density-based algorithms for noise elimination in general has long been demonstrated. The challenge of applying such techniques to time-series data consists in first specifying uninteresting sequences that are to be considered as noise, and secondly ensuring that those uninteresting sequences will not a#ect the clustering result. Both problems are addressed in this paper and the success of the technique is demonstrated on several standard data sets.