DMCA
Model-based clustering of time series in group-specific functional subspaces
| Venue: | Advances in Data Analysis and Classification |
| Citations: | 7 - 6 self |
BibTeX
@INPROCEEDINGS{Bouveyron_model-basedclustering,
author = {Charles Bouveyron and Julien Jacques and Université Lille and Inria Lille-nord Europe},
title = {Model-based clustering of time series in group-specific functional subspaces},
booktitle = {Advances in Data Analysis and Classification},
year = {},
pages = {2011}
}
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Abstract
This work develops a general procedure for clustering functional data which adapts the clustering method High Dimensional Data Clustering (HDDC), originally proposed in the multivariate context. The resulting clustering method, called funHDDC, is based on a functional latent mixture model which fits the functional data in group-specific functional subspaces. By constraining model parameters within and between groups, a family of parsimonious models is exhibited which allow to fit onto various situations. An estimation procedure based on the EM algorithm is proposed for determinating both the model parameters and the group-specific functional subspaces. Experiments on realworld datasets show that the proposed approach performs better or similarly than classical two-step clustering methods while providing useful interpretations of the groups and avoiding the uneasy choice of the discretization technique. In particular, funHDDC appears to always outperform HDDC applied on spline coefficients. 1
Keyphrases
group-specific functional subspace time series model-based clustering model parameter functional data clustering method various situation functional latent mixture model estimation procedure multivariate context classical two-step spline coefficient em algorithm uneasy choice discretization technique parsimonious model realworld datasets useful interpretation general procedure
