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JMLR
2010
211views more  JMLR 2010»
14 years 4 months ago
Minimum Conditional Entropy Clustering: A Discriminative Framework for Clustering
In this paper, we introduce an assumption which makes it possible to extend the learning ability of discriminative model to unsupervised setting. We propose an informationtheoreti...
Bo Dai, Baogang Hu
ISBI
2008
IEEE
15 years 10 months ago
A mathematical framework for incorporating anatomical knowledge in DT-MRI analysis
We propose a Bayesian approach to incorporate anatomical information in the clustering of fiber trajectories. An expectationmaximization (EM) algorithm is used to cluster the traj...
Carl-Fredrik Westin, Lilla Zöllei, Mahnaz Mad...
SIGKDD
2008
125views more  SIGKDD 2008»
14 years 9 months ago
Incremental pattern discovery on streams, graphs and tensors
Incremental pattern discovery targets streaming applications where the data continuously arrive incrementally. The questions are how to find patterns (main trends) incrementally; ...
Jimeng Sun
ISBI
2004
IEEE
15 years 10 months ago
Clustering-Based Framework for Comparing fMRI Analysis Methods
In this paper, a cluster-based framework is introduced for comparing analysis methods of functional magnetic resonance images (fMRI). In the proposed framework, fMRI data is repla...
Hamid Soltanian-Zadeh, Gholam-Ali Hossein-Zadeh, A...
JMLR
2010
130views more  JMLR 2010»
14 years 4 months ago
MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering
Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal...
Albert Bifet, Geoff Holmes, Bernhard Pfahringer, P...