Functional Data Clustering for High Dimensional Data with Outliers

Abstract

A clustering method for high dimensional data is very difficult because of the curse of dimensionality. In order to reduce the dimension of data, we use the functional data which is obtained by projecting data on a set of basis functions. There are three approaches on functional data clustering: 2-step methods, Nonparametric clustering, Model-based clustering. However, there are few studies on how to cluster the high dimensional data with outliers. In this paper, we suggest new robust functional clustering method using ES-algorithm and k-means clustering.