C4.5: programs for machine learning
C4.5: programs for machine learning
Elements of machine learning
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Knowledge Acquisition Via Incremental Conceptual Clustering
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
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
Hi-index | 0.00 |
Although feature selection is a central problem in inductive learning as suggested by the growing amount of research in this area, most of the work has been carried out under the supervised learning paradigm, paying little attention to unsupervised learning tasks and, particularly, clustering tasks. In this paper, we analyze the particular benefits that feature selection may provide in hierarchical clustering. We propose a view of feature selection as a tree pruning process similar to those used in decision tree learning. Under this framework, we perform several experiments using different pruning strategies and considering a multiple prediction task. Results suggest that hierarchical clusterings can be greatly simplified without diminishing accuracy.