Elements of machine learning
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
Applying AI Clustering to Engineering Tasks
IEEE Expert: Intelligent Systems and Their Applications
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Determining Property Relevance in Concept Formation by Computing Correlation Between Properties
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Efficient Feature Selection in Conceptual Clustering
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Feature Selection as a Preprocessing Step for Hierarchical Clustering
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Dependency-based feature selection for clustering symbolic data
Intelligent Data Analysis
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
Inductive querying for discovering subgroups and clusters
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering
Computer Methods and Programs in Biomedicine
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Feature selection has received a lot of attention in the machine learning community, but mainly under the supervised paradigm. In this work we study the potential benefits of feature selection in hierarchical clustering tasks. Particularly we address this problem in the context of incremental clustering, following the basic ideas of Gennari [8]. By using a simple implementation, we show that a feature selection scheme running in parallel with the learning process can improve the clustering task under the dimensions of accuracy, efficiency in learning, efficiency in prediction and comprehensibility.