Evaluation of k-means time series clustering based on z-normalization and NP-Free

• URL: http://arxiv.org/abs/2401.15773v1
• Date: Sun, 28 Jan 2024 21:23:13 GMT
• Title: Evaluation of k-means time series clustering based on z-normalization and NP-Free
• Authors: Ming-Chang Lee, Jia-Chun Lin, and Volker Stolz
• Abstract summary: This paper conducts 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 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.
• Score: 0.5898893619901381
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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