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ICRA
2007
IEEE
126views Robotics» more  ICRA 2007»
16 years 1 months ago
Incremental Spectral Clustering and Its Application To Topological Mapping
Abstract— This paper presents a novel use of spectral clustering algorithms to support cases where the entries in the affinity matrix are costly to compute. The method is increm...
Christoffer Valgren, Tom Duckett, Achim J. Lilient...
WILF
2007
Springer
98views Fuzzy Logic» more  WILF 2007»
16 years 24 days ago
Possibilistic Clustering in Feature Space
In this paper we propose the Possibilistic C-Means in Feature Space and the One-Cluster Possibilistic C-Means in Feature Space algorithms which are kernel methods for clustering in...
Maurizio Filippone, Francesco Masulli, Stefano Rov...
IJCNN
2006
IEEE
16 years 22 days ago
Information Theoretic Angle-Based Spectral Clustering: A Theoretical Analysis and an Algorithm
— Recent work has revealed a close connection between certain information theoretic divergence measures and properties of Mercer kernel feature spaces. Specifically, it has been...
Robert Jenssen, Deniz Erdogmus, Jose C. Principe
ICTAI
2005
IEEE
16 years 9 days ago
Determining the Optimal Number of Clusters Using a New Evolutionary Algorithm
Estimating the optimal number of clusters for a dataset is one of the most essential issues in cluster analysis. An improper pre-selection for the number of clusters might easily ...
Wei Lu, Issa Traoré
CLUSTER
2002
IEEE
15 years 11 months ago
Supermon: A High-Speed Cluster Monitoring System
Supermon is a flexible set of tools for high speed, scalable cluster monitoring. Node behavior can be monitored much faster than with other commonly used methods (e.g., rstatd). ...
Matthew J. Sottile, Ronald Minnich