Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth 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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Novel Unsupervised Feature Filtering of Biological Data
Bioinformatics
Consensus unsupervised feature ranking from multiple views
Pattern Recognition Letters
Weighted cluster ensembles: Methods and analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
Unsupervised feature weighting with multi niche crowding genetic algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Forward semi-supervised feature selection
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
| Hi-index | 0.00 |
The work in unsupervised learning centered on clustering has been extended with new paradigms to address the demands raised by real-world problems. In this regard, unsupervised feature selection has been proposed to remove noisy attributes that could mislead the clustering procedures. Additionally, semi-supervision has been integrated within existing paradigms because some background information usually exist in form of a reduced number of similarity/dissimilarity constraints. In this context, the current paper investigates a method to perform simultaneously feature selection and clustering. The benefits of a semi-supervised approach making use of reduced external information are highlighted against an unsupervised approach. The method makes use of an ensemble of near-optimal feature subsets delivered by a multi-modal genetic algorithm in order to quantify the relative importance of each feature to clustering.