This paper discusses the clustering quality and complexities of the hierarchical data clustering algorithm based on gravity theory. The gravitybased clustering algorithm simulates ...
In this paper we will describe Berkeley's approach to the Domain Specific (DS) track for CLEF 2007. This year we are using forms of the Entry Vocabulary Indexes and Thesaurus...
Abstract. This paper centers on a novel data mining technique we term supervised clustering. Unlike traditional clustering, supervised clustering is applied to classified examples ...
The Self-Organizing map (SOM), a powerful method for data mining and cluster extraction, is very useful for processing data of high dimensionality and complexity. Visualization met...
This paper presents a learning theoretical analysis of correlation clustering (Bansal et al., 2002). In particular, we give bounds on the error with which correlation clustering r...