Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology

Abstract Time-series clustering is a powerful data mining technique for time-series data in the absence of prior knowledge about the clusters. This study proposes a novel time-series clustering method that leverages a simulated annealing machine, which accurately solves combinatorial optimization problems. The proposed method facilitates an even classification of time-series data into clusters close to each other while maintaining robustness against outliers. We compared the proposed method with a standard existing method for clustering an online distributed dataset and found that both methods yielded comparable results. Furthermore, the proposed method was applied to a flow measurement image dataset containing noticeable noise with a signal-to-noise ratio of approximately 1. Despite a small signal variation of approximately 2%, the proposed method effectively classified the data without any overlap among the clusters. In contrast, the clustering results by the standard existing methods displayed overlapping clusters. These results indicate the effectiveness of the proposed method.