  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.
