
Building Clusters with Lower-Bounded Sizes
Abu-Khzam, Faisal N.; Bazgan, Cristina; Casel, Katrin; Fernau, Henning (2016), Building Clusters with Lower-Bounded Sizes, in 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 / ConférenceDate
2016Conference title
27th International Symposium on Algorithms and Computation, ISAAC 2016Conference date
2016-12Conference city
SydneyConference country
AustraliaBook title
27th International Symposium on Algorithms and Computation, ISAAC 2016Book author
Hong, Seok-HeePublisher
Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik
Published in
Wadern (Germany)
ISBN
978-3-95977-026-2
Pages
148:1–148:12
Publication identifier
Metadata
Show full item recordAuthor(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
Abstract (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.Subjects / Keywords
Clustering; Approximation Algorithms; Complexity; MatchingRelated items
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