We present two results which arise from a model-based approach to hierarchical agglomerative clustering. First, we show formally that the common heuristic agglomerative clustering...
Sepandar D. Kamvar, Dan Klein, Christopher D. Mann...
Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data in...
Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan ...
Abstract. A clustering method is presented which can be applied to semantically annotated resources in the context of ontological knowledge bases. This method can be used to discov...
In this paper we study the k-means clustering problem. It is well-known that the general version of this problem is NP-hard. Numerous approximation algorithms have been proposed fo...
We present an evolutionary clustering method which can be applied to multi-relational knowledge bases storing resource annotations expressed in the standard languages for the Sema...