Co-clustering of time-dependent data via Shape Invariant Model

04/07/2021
by   Alessandro Casa, et al.
0

Multivariate time-dependent data, where multiple features are observed over time for a set of individuals, are increasingly widespread in many application domains. To model these data we need to account for relations among both time instants and variables and, at the same time, for subjects heterogeneity. We propose a new co-clustering methodology for clustering individuals and variables simultaneously that is designed to handle both functional and longitudinal data. Our approach borrows some concepts from the curve registration framework by embedding the Shape Invariant Model in the Latent Block Model, estimated via a suitable modification of the SEM-Gibbs algorithm. The resulting procedure allows for several user-defined specifications of the notion of cluster that could be chosen on substantive grounds and provides parsimonious summaries of complex longitudinal or functional data by partitioning data matrices into homogeneous blocks.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/26/2018

Block models for multipartite networks.Applications in ecology and ethnobiology

Modeling relations between individuals is a classical question in social...
research
11/04/2018

Cross-Component Registration for Multivariate Functional Data with Application to Longitudinal Growth Curves

Multivariate functional data are becoming ubiquitous with the advance of...
research
05/17/2020

Model-Based Longitudinal Clustering with Varying Cluster Assignments

It is often of interest to perform clustering on longitudinal data, yet ...
research
11/13/2017

Simultaneous Registration and Clustering for Multi-dimensional Functional Data

The clustering for functional data with misaligned problems has drawn mu...
research
12/20/2018

Block clustering of Binary Data with Gaussian Co-variables

The simultaneous grouping of rows and columns is an important technique ...
research
07/03/2023

Conditional partial exchangeability: a probabilistic framework for multi-view clustering

Standard clustering techniques assume a common configuration for all fea...

Please sign up or login with your details

Forgot password? Click here to reset