Time Series Clustering With Random Convolutional Kernels

TL;DR

This paper introduces R-Clustering, a new time series clustering method using random convolutional kernels, which outperforms existing techniques in accuracy, efficiency, and scalability across diverse datasets.

Contribution

The paper presents R-Clustering, a novel approach leveraging random convolutional architectures for improved time series clustering performance.

Findings

R-Clustering achieves higher accuracy than existing methods.

It demonstrates better computational efficiency and scalability.

Effective across diverse datasets from multiple domains.

Abstract

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.