José G. Dias

15 papers Journal 9Unranked 4
YearRankTypeTitle / Venue / Authors
2021 J jnl
MAINT.Data: Modelling and Analysing Interval Data in R.
R J.
António Pedro Duarte Silva, Paula Brito, Peter Filzmoser, José G. Dias
2017 J jnl
Extracting clusters from aggregate panel data: A market segmentation study.
Appl. Math. Comput.
Graça Trindade, José G. Dias, Jorge A. Ambrósio
2015 J jnl
Clustering financial time series: New insights from an extended hidden Markov model.
Eur. J. Oper. Res.
José G. Dias, Jeroen K. Vermunt, Sofia Ramos
2015 J jnl
Probabilistic clustering of interval data.
Intell. Data Anal.
Paula Brito, António Pedro Duarte Silva, José G. Dias
2015 J jnl
Survival mixture models in behavioral scoring.
Expert Syst. Appl.
Bruno Cardoso Alves, José G. Dias
2014 J jnl
Dynamic clustering of energy markets: An extended hidden Markov approach.
Expert Syst. Appl.
José G. Dias, Sofia Ramos
2014 J jnl
Mining categorical sequences from data using a hybrid clustering method.
Eur. J. Oper. Res.
Luca De Angelis, José G. Dias
2013 — ch.
Latent Class Models of Time Series Data: An Entropic-Based Uncertainty Measure.
Algorithms from and for Nature and Life
José G. Dias
2013 — ch.
Model-Based Clustering of Multistate Data with Latent Change: An Application with DHS Data.
Classification and Data Mining
José G. Dias
2008 J jnl
A bootstrap-based aggregate classifier for model-based clustering.
Comput. Stat.
José G. Dias, Jeroen K. Vermunt
2008 — conf
Mixture Hidden Markov Models in Finance Research.
GfKl
José G. Dias, Jeroen K. Vermunt, Sofia Ramos
2008 — conf
The SKM Algorithm: A K-Means Algorithm for Clustering Sequential Data.
IBERAMIA
José G. Dias, Maria João Cortinhal
2006 — conf
Model Selection Criteria for Model-Based Clustering of Categorical Time Series Data: A Monte Carlo Study.
GfKl
José G. Dias
2004 J jnl
An empirical comparison of EM, SEM and MCMC performance for problematic Gaussian mixture likelihoods.
Stat. Comput.
José G. Dias, Michel Wedel
2004 — conf
Bootstrapping Latent Class Models.
GfKl
José G. Dias