Moving Functional k-means Clustering and Its Application

Abstract

A novel functional clustering method for domain-dependent functional data is proposed. The functional data are defined on sequential domains and they might have particular clusters on each domain. Our approach, Moving Functional k-means Clustering, is to classify the peculiar domains based on each sequential set of clusters. A typical functional clustering with fixed number of clusters is applied to the domains, then we relabel the set of clusters to keep the previous labels as many as possible. We demonstrate our method with large scale sensing data of environmental radio activity level in Fukushima Prefecture.