A hybrid clustering algorithm

Authors:
Kweku-Muata Osei-Bryson;Tasha R. Inniss
Affiliations:
Department of Information Systems & The Information Systems Research Institute, Virginia Commonwealth University, Richmond, VA 23284, USA;Department of Mathematics, Spelman College, Atlanta, GA 30314-4399, USA
Venue:
Computers and Operations Research
Year:
2007

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Abstract

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.