Proceedings of the 18th ACM conference on Information and knowledge management
Network topology discovery through self-constrained decisions
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Efficient and dynamic routing topology inference from end-to-end measurements
IEEE/ACM Transactions on Networking (TON)
Merging spanning trees in tomographic network topology discovery
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Toward the practical use of network tomography for internet topology discovery
INFOCOM'10 Proceedings of the 29th conference on Information communications
Two-dimensional clustering algorithms for image segmentation
WSEAS Transactions on Computers
Efficient network tomography for internet topology discovery
IEEE/ACM Transactions on Networking (TON)
Towards hierarchical clustering
CSR'07 Proceedings of the Second international conference on Computer Science: theory and applications
A spectral-multiplicity-tolerant approach to robust graph matching
Pattern Recognition
| Hi-index | 35.68 |
This paper develops a new method for hierarchical clustering. Unlike other existing clustering schemes, our method is based on a generative, tree-structured model that represents relationships between the objects to be clustered, rather than directly modeling properties of objects themselves. In certain problems, this generative model naturally captures the physical mechanisms responsible for relationships among objects, for example, in certain evolutionary tree problems in genetics and communication network topology identification. The paper examines the networking problem in some detail to illustrate the new clustering method. More broadly, the generative model may not reflect actual physical mechanisms, but it nonetheless provides a means for dealing with errors in the similarity matrix, simultaneously promoting two desirable features in clustering: intraclass similarity and interclass dissimilarity.