We propose a spectral learning approach to shape segmentation. The method is composed of a constrained spectral clustering algorithm that is used to supervise the segmentation of a...
We study a number of open issues in spectral clustering: (i) Selecting the appropriate scale of analysis, (ii) Handling multi-scale data, (iii) Clustering with irregular backgroun...
We propose a scalable technique called Seeded Clustering that allows us to maintain R-tree indices by bulk insertion while keeping pace with high data arrival rates. Our approach ...
In this paper, we propose a novel non-parametric clustering method based on non-parametric local shrinking. Each data point is transformed in such a way that it moves a specific ...
Additive clustering was originally developed within cognitive psychology to enable the development of featural models of human mental representation. The representational flexibili...