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. © 2005 IEEE.
| Original language | English |
|---|---|
| Title of host publication | 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings|Congr. Evol. Comput. Proc. |
| Pages | 632-639 |
| Number of pages | 7 |
| Volume | 1 |
| Publication status | Published - 2005 |
| Event | 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland Duration: 1 Jul 2005 → … |
Conference
| Conference | 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 |
|---|---|
| City | Edinburgh, Scotland |
| Period | 1/07/05 → … |