Multi-Dimensional Temporal Feature Fusion and Density Perception for Time Series Clustering

Abstract

In the field of data mining and knowledge discovery, clustering algorithms have emerged as a powerful tool for unsupervised learning. The adaptability and efficiency of these algorithms make them indispensable in a multitude of applications, including customer segmentation in marketing and anomaly detection in cybersecurity. However, when these clustering algorithms are applied to time series data, a number of distinctive challenges emerge. The representation of time series data, which is often vast and high-dimensional, requires the application of efficient techniques that reduce the dimensionality of the data while ensuring the preservation of vital information. Furthermore, existing clustering methods encounter difficulties when dealing with variable density distributions. In response to these challenges, we present the Density-based Clustering Model for Time Series (DCMD). This model seamlessly integrates temporal representation and clustering, ensuring efficiency and accuracy. Our Multi-dimensional Representation Fusion (MDR) method for time series retains critical features while reducing data dimensions. Furthermore, the K-Nearest Neighbor Weighted (NNW) clustering method enhances local density calculation. Rigorous benchmark evaluations validate the efficacy of our approach. Our contributions advance the field of time series clustering research and show promise for diverse applications.