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» Model-based clustering for longitudinal data
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CSDA
2008
91views more  CSDA 2008»
13 years 4 months ago
Model-based clustering for longitudinal data
A model-based clustering method is proposed for clustering individuals on the basis of measurements taken over time. Data variability is taken into account through non-linear hier...
Rolando De la Cruz-Mesía, Fernando A. Quint...
CSDA
2008
98views more  CSDA 2008»
13 years 4 months ago
Forecasting binary longitudinal data by a functional PC-ARIMA model
The purpose of this paper is to forecast the time evolution of a binary response variable from an associated continuous time series observed only at discrete time points that usual...
Ana M. Aguilera, Manuel Escabias, Mariano J. Valde...
DAGM
2008
Springer
13 years 6 months ago
Boosting for Model-Based Data Clustering
In this paper a novel and generic approach for model-based data clustering in a boosting framework is presented. This method uses the forward stagewise additive modeling to learn t...
Amir Saffari, Horst Bischof
KDD
2003
ACM
191views Data Mining» more  KDD 2003»
14 years 4 months ago
Assessment and pruning of hierarchical model based clustering
The goal of clustering is to identify distinct groups in a dataset. The basic idea of model-based clustering is to approximate the data density by a mixture model, typically a mix...
Jeremy Tantrum, Alejandro Murua, Werner Stuetzle
ICML
2010
IEEE
13 years 5 months ago
Variable Selection in Model-Based Clustering: To Do or To Facilitate
Variable selection for cluster analysis is a difficult problem. The difficulty originates not only from the lack of class information but also the fact that high-dimensional data ...
Leonard K. M. Poon, Nevin Lianwen Zhang, Tao Chen,...