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Building Clusters with Lower-Bounded Sizes

Abu-Khzam, Faisal N.; Bazgan, Cristina; Casel, Katrin; Fernau, Henning (2016), Building Clusters with Lower-Bounded Sizes, dans Hong, Seok-Hee, 27th International Symposium on Algorithms and Computation, ISAAC 2016, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik : Wadern (Germany), p. 148:1–148:12. 10.4230/LIPIcs.ISAAC.2016.148

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Type
Communication / Conférence
Date
2016
Titre du colloque
27th International Symposium on Algorithms and Computation, ISAAC 2016
Date du colloque
2016-12
Ville du colloque
Sydney
Pays du colloque
Australia
Titre de l'ouvrage
27th International Symposium on Algorithms and Computation, ISAAC 2016
Auteurs de l’ouvrage
Hong, Seok-Hee
Éditeur
Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik
Ville d’édition
Wadern (Germany)
Isbn
978-3-95977-026-2
Pages
148:1–148:12
Identifiant publication
Métadonnées
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Auteur(s)
Abu-Khzam, Faisal N.
Lebanese American University [LAU]
Bazgan, Cristina
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Casel, Katrin

Fernau, Henning
Résumé (EN)
Classical clustering problems search for a partition of objects into a fixed number of clusters. In many scenarios however the number of clusters is not known or necessarily fixed. Further, clusters are sometimes only considered to be of significance if they have a certain size. We discuss clustering into sets of minimum cardinality k without a fixed number of sets and present a general model for these types of problems. This general framework allows the comparison of different measures to assess the quality of a clustering. We specifically consider nine quality-measures and classify the complexity of the resulting problems with respect to k. Further, we derive some polynomial-time solvable cases for k = 2 with connections to matching-type problems which, among other graph problems, then are used to compute approximations for larger values of k.
Mots-clés
Clustering; Approximation Algorithms; Complexity; Matching
JEL
C44 - Operations Research; Statistical Decision Theory