Iterative Rank based Methods for Clustering
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
A parallel hybrid web document clustering algorithm and its performance study
The Journal of Supercomputing - Special issue: Parallel and distributed processing and applications
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
Constructing the R* consensus tree of two trees in subcubic time
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Predicting the labels of an unknown graph via adaptive exploration
Theoretical Computer Science
| Hi-index | 0.00 |
A cluster A is an Apresjan cluster if every pair of objects within A is more similar than either is to any object outside A. The criterion is intuitive, compelling, but often too restrictive for applications in classification. We therefore explore extensions of Apresjan clustering to a family of related hierarchical clustering methods. The extensions are shown to be closely connected with the well-known single and average linkage tree constructions. A dual family of methods for classification by splits is also presented. Splits are partitions of the set of objects into two disjoint blocks and are widely used in domains such as phylogenetics. Both the cluster and split methods give rise to progressively refined tree representations. We exploit dualities and connections between the various methods, giving polynomial time construction algorithms for most of the constructions and NP-hardness results for the rest.