Evaluation of k-means time series clustering based on z-normalization
  and NP-Free
Ming-Chang Lee Jia-Chun Lin Volker Stolz
Despite the widespread use of k-means time series clustering in various
domains, there exists a gap in the literature regarding its comprehensive
evaluation with different time series normalization approaches. This paper
seeks to fill this gap by conducting a thorough performance evaluation of
k-means time series clustering on real-world open-source time series datasets.
The evaluation focuses on two distinct normalization techniques:
z-normalization and NP-Free. The former is one of the most commonly used
normalization approach for time series. The latter is a real-time time series
representation approach, which can serve as a time series normalization
approach. The primary objective of this paper is to assess the impact of these
two normalization techniques on k-means time series clustering in terms of its
clustering quality. The experiments employ the silhouette score, a
well-established metric for evaluating the quality of clusters in a dataset. By
systematically investigating the performance of k-means time series clustering
with these two normalization techniques, this paper addresses the current gap
in k-means time series clustering evaluation and contributes valuable insights
to the development of time series clustering.