Multiobjective clustering around medoids

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The large majority of existing clustering algorithms are centered around the notion of a feature, that is, individual data items are represented by their intrinsic properties, which are summarized by (usually numeric) feature vectors. However, certain applications require the clustering of data items that are defined by exclusively extrinsic properties: only the relationships between individual data items are known (that is, their similarities or dissimilarities). This paper develops a straightforward and efficient adaptation of our existing multiobjective clustering algorithm to such a scenario. The resulting algorithm is demonstrated on a range of data sets, including a dissimilarity matrix derived from real, non-feature-based data. © 2005 IEEE.
Original languageEnglish
Title of host publication2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings|Congr. Evol. Comput. Proc.
Number of pages7
Publication statusPublished - 2005
Event2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland
Duration: 1 Jul 2005 → …


Conference2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005
CityEdinburgh, Scotland
Period1/07/05 → …


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