Feature selection with neural networks
Pattern Recognition Letters
An adaptation of Relief for attribute estimation in regression
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Subset Selection and Ranking for Data Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Joint cluster based co-clustering for clustering ensembles
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Projected Gustafson-Kessel clustering algorithm and its convergence
Transactions on rough sets XIV
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A new feature selection algorithm for unsupervised learning is proposed. It is based on the assumption that, in absence of class labels, the clustering ensemble result can be employed as a heuristic to guide the feature selection. Therefore, a modified RReliefF algorithm is then used to assign the rankings for every feature. The main advantage of the proposed unsupervised feature selection algorithm in comparison to conventional schemes is that it is dimensionality unbiased. Our experiments with several data sets demonstrate that the proposed algorithm is able to detect completely irrelevant features and to remove some additional features without significantly hurting the performance of the clustering algorithm.