Evaluation of K-Means Time Series Clustering Based on Z-Normalization and NP-Free

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 preprocessing 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 techniques: z-normalization and NP-Free. The former is one of the most commonly used approaches for normalizing time series, and the latter is a real-time time series representation approach. The primary objective of this paper is to assess the impact of these two 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 preprocessing tech niques, this paper addresses the current gap in k-means time series clustering evaluation and contributes valuable insights to the development of time series clustering