Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Floating search methods in feature selection
Pattern Recognition Letters
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
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Feature subset selection using a new definition of classifiability
Pattern Recognition Letters
Feature Weighting in k-Means Clustering
Machine Learning
Feature Selection for Clustering - A Filter Solution
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
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
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Robust subspace clustering by combined use of kNND metric and SVD algorithm
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Adapt the mRMR criterion for unsupervised feature selection
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Robust local feature weighting hard c-means clustering algorithm
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Hi-index | 0.10 |
In clustering, global feature selection algorithms attempt to select a common feature subset that is relevant to all clusters. Consequently, they are not able to identify individual clusters that exist in different feature subspaces. In this paper, we propose a localized feature selection algorithm for clustering. The proposed algorithm computes adjusted and normalized scatter separability for individual clusters. A sequential backward search is then applied to find the optimal (maybe local) feature subsets for each cluster. Our experimental results show the need for feature selection in clustering and the benefits of selecting features locally.