Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
An experimental comparison of model-based clustering methods
Machine Learning
Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Feature Selection as a Preprocessing Step for Hierarchical Clustering
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Model Selection in Unsupervised Learning with Applications To Document Clustering
ICML '99 Proceedings of the Sixteenth 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
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary model selection in unsupervised learning
Intelligent Data Analysis
Dependency-based feature selection for clustering symbolic data
Intelligent Data Analysis
Identifying user preferences with Wrapper-based Decision Trees
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Advanced Engineering Informatics
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Feature selection for clustering is a problem rarely addressed in the literature. Although recently there has been some work on the area, there is a lack of extensive empirical evaluation to assess the potential of each method. In this paper, we propose a new implementation of a wrapper and adapt an existing filter method to perform experiments over several data sets and compare both approaches. Results confirm the utility of feature selection for clustering and the theoretical superiority of wrapper methods. However, it raises some problems that arise from using greedy search procedures and also suggest evidence that filters are a reasonably alternative with limited computational cost.