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Advances in Data Analysis and Classification

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01-03-2019 | Editorial

Special issue on “Advances on model-based clustering and classification”

Preface by the Guest Editors

Authors: Sylvia Frühwirth-Schnatter, Salvatore Ingrassia, Agustín Mayo-Iscar

Published in: Advances in Data Analysis and Classification | Issue 1/2019

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This Special Issue of ADAC is devoted to recent developments in Model-Based Clustering and Classification which is an increasingly active area in both theoretical and applied research. This area has attracted the interest of a growing number of researchers due to the high potential of such approaches in applications, and new different topics have been investigated by several authors. This special issue contains 13 papers, that have been accepted for publication after a blinded peer-reviewed process, dealing with quite different topics like mixture models for both continuous and discrete data, co-clustering, robust approaches to clustering data, clustering time series, agreement measure for class partitions, and various applications in the framework of model-based clustering. Below, we provide a short overview of the papers published in this special issue. …

next article Unifying data units and models in (co-)clustering

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Title: Special issue on “Advances on model-based clustering and classification”
Preface by the Guest Editors
Authors: Sylvia Frühwirth-Schnatter
Salvatore Ingrassia
Agustín Mayo-Iscar
Publication date: 01-03-2019
Publisher: Springer Berlin Heidelberg
Published in: Advances in Data Analysis and Classification / Issue 1/2019
Print ISSN: 1862-5347
Electronic ISSN: 1862-5355
DOI: https://doi.org/10.1007/s11634-019-00355-w

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Other articles of this Issue 1/2019

Finite mixtures, projection pursuit and tensor rank: a triangulation

Variable selection in model-based clustering and discriminant analysis with a regularization approach

Clustering via finite nonparametric ICA mixture models

Robust clustering for functional data based on trimming and constraints

Studying crime trends in the USA over the years 2000–2012

Finite mixture of regression models for censored data based on scale mixtures of normal distributions

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