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» A Distance-Based Packing Method for High Dimensional Data
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DAGM
2010
Springer
13 years 10 months ago
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...
Hannes Nickisch, Carl Edward Rasmussen
SDM
2008
SIAM
256views Data Mining» more  SDM 2008»
13 years 10 months ago
Graph Mining with Variational Dirichlet Process Mixture Models
Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high ...
Koji Tsuda, Kenichi Kurihara
SDM
2008
SIAM
157views Data Mining» more  SDM 2008»
13 years 10 months ago
ROC-tree: A Novel Decision Tree Induction Algorithm Based on Receiver Operating Characteristics to Classify Gene Expression Data
Gene expression information from microarray experiments is a primary form of data for biological analysis and can offer insights into disease processes and cellular behaviour. Suc...
M. Maruf Hossain, Md. Rafiul Hassan, James Bailey
EPIA
2005
Springer
14 years 2 months ago
An Extension of Self-organizing Maps to Categorical Data
Self-organizing maps (SOM) have been recognized as a powerful tool in data exploratoration, especially for the tasks of clustering on high dimensional data. However, clustering on ...
Ning Chen, Nuno C. Marques
ESANN
2004
13 years 10 months ago
Clustering functional data with the SOM algorithm
Abstract. In many situations, high dimensional data can be considered as sampled functions. We show in this paper how to implement a Self-Organizing Map (SOM) on such data by appro...
Fabrice Rossi, Brieuc Conan-Guez, Aïcha El Go...