Abstract
In this paper, we introduce a new approach called Competitive Agglomeration (CA), which combines the advantages of hierarchical and partitional clustering techniques. The CA algorithm starts by partitioning the data set into a large number of small clusters. As the algorithm progresses, adjacent clusters compete for data points, and clusters that lose in the competition gradually become depleted and vanish. Thus, as the iterations proceed, we obtain a sequence of partitions with a progressively diminishing number of clusters. The final partition is taken to have the optimal number of clusters from the point of view of the objective function. Since the algorithm starts with an overspecified number of clusters, the final results are quite insensitive to the initialization and to local minima. In addition, we can incorporate different distance measures in the objective function of the CA algorithm to find an unknown number of clusters of various shapes. We illustrate the performance of the CA algorithm for the special cases of spherical, ellipsoidal, linear, and shell clusters.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.