Visualization and interactive feature selection for unsupervised data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature selection in unsupervised learning via evolutionary search
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Feature Selection in Conceptual Clustering
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Feature Selection and Incremental Learning of Probabilistic Concept Hierarchies
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Selection for Clustering
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Feature Selection for Clustering - A Filter Solution
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Improved Visual Clustering through Unsupervised Dimensionality Reduction
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Simultaneous model selection and feature selection via BYY harmony learning
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
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High dimensional data is a challenge for the KDD community. Feature Selection (FS) is an efficient preprocessing step for dimensionality reduction thanks to the removal of redundant and/or noisy features. Few and mostly recent FS methods have been proposed for clustering. Furthermore, most of them are ”wrapper” methods that require the use of clustering algorithms for evaluating the selected features subsets. Due to this reliance on clustering algorithms that often require parameters settings (such as number of clusters), and due to the lack of a consensual suitable criterion to evaluate clustering quality in different subspaces, the wrapper approach cannot be considered as a universal way to perform FS within the clustering framework. Thus, we propose and evaluate in this paper a ”filter” FS method. This approach is consequently completely independent of any clustering algorithm. It is based upon the use of two specific indices that allow to assess the adequacy between two sets of features. As these indices exhibit very specific and interesting properties as far as their computational cost is concerned (they just require one dataset scan), the proposed method can be considered as an effective method not only from the point of view of the results quality but also from the execution time point of view.