  Time-series clustering serves as a powerful data mining technique for
time-series data in the absence of prior knowledge about clusters. A large
amount of time-series data with large size has been acquired and used in
various research fields. Hence, clustering method with low computational cost
is required. Given that a quantum-inspired computing technology, such as a
simulated annealing machine, surpasses conventional computers in terms of fast
and accurately solving combinatorial optimization problems, it holds promise
for accomplishing clustering tasks that are challenging to achieve using
existing methods. This study proposes a novel time-series clustering method
that leverages an annealing machine. The proposed method facilitates an even
classification of time-series data into clusters close to each other while
maintaining robustness against outliers. Moreover, its applicability extends to
time-series images. We compared the proposed method with a standard existing
method for clustering an online distributed dataset. In the existing method,
the distances between each data are calculated based on the Euclidean distance
metric, and the clustering is performed using the k-means++ method. We 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 method and the conditional image sampling
(CIS) method, a specialized technique for flow measurement data, displayed
overlapping clusters. Consequently, the proposed method provides better results
than the other two methods, demonstrating its potential as a superior
clustering method.
