Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation

Abstract

We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into $K$ clusters and represents each cluster by a simple prototype (e.g., piecewise constant). The total number of segments in the prototypes, $P$, is chosen by the user and optimally distributed among the clusters via two dynamic programming algorithms. The practical relevance of the method is shown on two real world datasets.

References in corpus (3)

• Batch and median neural gas
• Fast Selection of Spectral Variables with B-Spline Compression
• A data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysis