Parsimonious Time Series Clustering

TL;DR

This paper presents a new parsimonious model-based approach for clustering noisy, sparse time series data using P-spline smoothers, improving efficiency and accuracy in biological applications.

Contribution

It introduces a spline-based clustering framework that reduces computational complexity and handles noisy, sparse data effectively, applicable within standard clustering methods.

Findings

Effective in clustering gene expression data

Improves computational efficiency

Maintains high clustering accuracy

Abstract

We introduce a parsimonious model-based framework for clustering time course data. In these applications the computational burden becomes often an issue due to the number of available observations. The measured time series can also be very noisy and sparse and a suitable model describing them can be hard to define. We propose to model the observed measurements by using P-spline smoothers and to cluster the functional objects as summarized by the optimal spline coefficients. In principle, this idea can be adopted within all the most common clustering frameworks. In this work we discuss applications based on a k-means algorithm. We evaluate the accuracy and the efficiency of our proposal by simulations and by dealing with drosophila melanogaster gene expression data.