Time series data, spanning applications ranging from climatology to finance to healthcare, presents significant challenges in data mining due to its size and complexity. One open issue lies in time series clustering, which is crucial for processing large volumes of unlabeled time series data and unlocking valuable insights. Traditional and modern analysis methods, however, often struggle with these complexities. To address these limitations, we introduce R-Clustering, a novel method that utilizes convolutional architectures with randomly selected parameters. Through extensive evaluations, R-Clustering demonstrates superior performance over existing methods in terms of clustering accuracy, computational efficiency and scalability. Empirical results obtained using the UCR archive demonstrate the effectiveness of our approach across diverse time series datasets. The findings highlight the significance of R-Clustering in various domains and applications, contributing to the advancement of time series data mining.

翻译：时间序列数据涵盖了从气候学到金融学再到医疗保健等多个领域的应用，由于其规模和复杂性，在数据挖掘中面临巨大挑战。其中一个悬而未决的问题在于时间序列聚类，这对于处理大量未标注的时间序列数据并挖掘有价值的信息至关重要。然而，传统和现代分析方法往往难以应对这些复杂性。为了解决这些限制，我们提出了R-Clustering，一种利用随机参数卷积架构的新方法。通过广泛评估，R-Clustering在聚类准确性、计算效率和可扩展性方面展现出优于现有方法的性能。使用UCR存档获得的实证结果验证了我们的方法在多种时间序列数据集上的有效性。这些发现凸显了R-Clustering在各个领域和应用中的重要性，为时间序列数据挖掘的进步做出了贡献。