The main idea of the proposed unsupervised feature selection algorithm is to search for a subset of all features such that the clustering algorithm trained on ...
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Sep 1, 2008 · The main idea of the proposed unsupervised feature selection algorithm is to search for a subset of all features such that the clustering ...
The main idea of the proposed unsupervised feature selection algorithm is to search for a subset of all features such that the clustering algorithm trained on ...
Dec 20, 2007 · This paper describes a novel feature selection algorithm for unsupervised clustering, that combines the clustering ensembles method and the ...
Bibliographic details on Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm.
Clustering Ensemble for Unsupervised Feature Selection | Semantic ...
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The proposed unsupervised feature selection algorithm is able to detect completely irrelevant features and to remove some additional features without ...
We propose a new method called Random Cluster Ensemble (RCE for short), that estimates the out-of-bag feature importance from an ensemble of partitions. Each ...
Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm · Resampling-based selective clustering ensembles.
Apr 3, 2013 · We propose a new method called Random Cluster Ensemble (RCE for short), that estimates the out-of-bag feature importance from an ensemble of ...
A new feature selection algorithm for unsupervised learning is proposed. It is based on the assumption that, in absence of class labels, the clustering ...