Discrete model-based clustering with overlapping subsets of attributes

Rodríguez-Sánchez, Fernando and Larrañaga Múgica, Pedro María and Bielza Lozoya, María Concepción (2018). Discrete model-based clustering with overlapping subsets of attributes. "Proceedings of Machine Learning Research (PMLR)", v. 72 ; pp. 392-403. ISSN 2640-3498.

Description

Title: Discrete model-based clustering with overlapping subsets of attributes
Author/s:
  • Rodríguez-Sánchez, Fernando
  • Larrañaga Múgica, Pedro María
  • Bielza Lozoya, María Concepción
Item Type: Article
Event Title: 9th International Conference on Probabilistic Graphical Models
Event Dates: 11-14 Sep 2018
Event Location: Praga, República Checa
Title of Book: Proceedings of Machine Learning Research (PMLR)
Título de Revista/Publicación: Proceedings of Machine Learning Research (PMLR)
Date: 2018
ISSN: 2640-3498
Volume: 72
Subjects:
Freetext Keywords: Multi-partition clustering; Overlapping latent class models; Model-based clustering; Discrete Bayesian networks
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Traditional model-based clustering methods assume that data instances can be grouped in a single “best” way. This is often untrue for complex data, where several meaningful sets of clusters may exist, each of them associated to a unique subset of data attributes. Current literature has approached this problem with models that consider disjoint subsets of attributes to define distinct clustering solutions. Each solution being represented by a cluster variable. However, restricting attributes to a single cluster variable diminishes the expressiveness and quality of these models. For this reason, we propose a novel kind of models that allows cluster variables to have overlapping subsets of attributes between them. In order to learn these models, we propose to combine a searchbased method with an attribute clustering procedure. Experimental results with both synthetic and real-world data show the utility of our approach and its competitiveness with the state-of-the-art.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainC080020-09UnspecifiedUnspecifiedCajal Blue Brain
Government of SpainTIN2016-79684-PUnspecifiedUniversidad Politécnica de MadridAvances en clasificación multidimensional y detección de anomalías con redes bayesianas
Madrid Regional GovernmentS2013/ICE-2845CASI – CAMUnspecifiedConceptos y aplicaciones de los sistemas inteligentes

More information

Item ID: 54652
DC Identifier: http://oa.upm.es/54652/
OAI Identifier: oai:oa.upm.es:54652
Official URL: http://proceedings.mlr.press/v72/rodriguez-sanchez18a/rodriguez-sanchez18a.pdf
Deposited by: Memoria Investigacion
Deposited on: 20 May 2019 06:54
Last Modified: 20 May 2019 06:54
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