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

arXiv: https://arxiv.org/abs/2305.16656 · HTML: https://mlsanity.com/paper/2305.16656/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 59 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 6 of 6)

- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [sktime: A Unified Interface for Machine Learning with Time Series](https://mlsanity.com/paper/1909.07872/) (2019)
- [Reservoir computing approaches for representation and classification of multivariate time series](https://mlsanity.com/paper/1803.07870/) (2018)
- [Temporal Phenotyping using Deep Predictive Clustering of Disease Progression](https://mlsanity.com/paper/2006.08600/) (2020)
- [Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space Models](https://mlsanity.com/paper/2208.11907/) (2022)
- [Data-Driven Optimal Sensor Placement for High-Dimensional System Using Annealing Machine](https://mlsanity.com/paper/2205.05430/) (2022)

## Built on by (most cited first, 3 of 3)

- [Data clustering: a fundamental method in data science and management](https://mlsanity.com/paper/2412.18760/) (2024)
- [CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series Clustering](https://mlsanity.com/paper/2505.14596/) (2025)
- [Towards Explainable Sequential Learning](https://mlsanity.com/paper/2505.23624/) (2025)

## Abstract (arXiv)

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.