Article ID: | iaor20084192 |
Country: | United Kingdom |
Volume: | 34 |
Issue: | 11 |
Start Page Number: | 3255 |
End Page Number: | 3269 |
Publication Date: | Nov 2007 |
Journal: | Computers and Operations Research |
Authors: | Osei-Bryson Kweku-Muata, Inniss Tasha R. |
Keywords: | programming: parametric |
Clustering attempts to partition a dataset into a meaningful set of mutually exclusive clusters. It is known that sequential clustering algorithms can give optimal partitions when applied to an ordered set of objects. In this technical note, we explore how this approach could be generalized to partition datasets in which there is no natural sequential ordering of the objects. As such, it extends the application of sequential clustering algorithms to all sets of objects.