Multi-facet determination for clustering with Bayesian networks

Rodríguez-Sánchez, Fernando and Larrañaga Múgica, Pedro and Bielza Lozoya, María Concepción (2017). Multi-facet determination for clustering with Bayesian networks. Monografía (Technical Report). E.T.S. de Ingenieros Informáticos (UPM).

Description

Title: Multi-facet determination for clustering with Bayesian networks
Author/s:
  • Rodríguez-Sánchez, Fernando
  • Larrañaga Múgica, Pedro
  • Bielza Lozoya, María Concepción
Item Type: Monograph (Technical Report)
Date: 2017
Subjects:
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

Real world applications of sectors like industry, healthcare or finance usually generate data of high complexity that can be interpreted from different viewpoints. When clustering this type of data, a single set of clusters may not suffice, hence the necessity of methods that generate multiple clusterings that represent different perspectives. In this paper, we present a novel multi-partition clustering method that returns several interesting and non-redundant solutions, where each of them is a data partition with an associated facet of data. Each of these facets represents a subset of the original attributes that is selected using our information-theoretic criterion UMRMR. Our approach is based on an optimization procedure that takes advantage of the Bayesian network factorization to provide high quality solutions in a fraction of the time.

More information

Item ID: 48177
DC Identifier: http://oa.upm.es/48177/
OAI Identifier: oai:oa.upm.es:48177
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 18 Oct 2017 09:35
Last Modified: 18 Oct 2017 09:35
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