%0 Journal Article %1 Handl05clusteringMedoids %A Handl, J. %A Knowles, J. %D 2005 %J Evolutionary Computation, 2005. The 2005 IEEE Congress on %K 05 Handl clustering medoids multiobjective %P 632-639 Vol.1 %R 10.1109/CEC.2005.1554742 %T Multiobjective clustering around medoids %V 1 %X 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.

@article{Handl05clusteringMedoids, abstract = { 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.}, added-at = {2008-12-06T00:24:37.000+0100}, author = {Handl, J. and Knowles, J.}, biburl = {https://www.bibsonomy.org/bibtex/20a8647daf0a6871fcc46b73e72f3bbff/lee_peck}, description = {Welcome to IEEE Xplore 2.0: Multiobjective clustering around medoids}, doi = {10.1109/CEC.2005.1554742}, interhash = {613af56304e0ee9c73def2472ed3f4b7}, intrahash = {0a8647daf0a6871fcc46b73e72f3bbff}, journal = {Evolutionary Computation, 2005. The 2005 IEEE Congress on}, keywords = {05 Handl clustering medoids multiobjective}, month = {Sept.}, pages = { 632-639 Vol.1}, timestamp = {2008-12-06T00:24:37.000+0100}, title = {Multiobjective clustering around medoids}, volume = 1, year = 2005 }