Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection as a Preprocessing Step for Hierarchical Clustering
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Feature Weighting in k-Means Clustering
Machine Learning
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Novel Unsupervised Feature Filtering of Biological Data
Bioinformatics
Evolutionary model selection in unsupervised learning
Intelligent Data Analysis
Comparison between two coevolutionary feature weighting algorithms in clustering
Pattern Recognition
Weighted cluster ensembles: Methods and analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
Weighted fuzzy c-means clustering based on double coding genetic algorithm
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Optimized ensembles for clustering noisy data
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
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This paper is concerned with feature weighting/selection in the context of unsupervised clustering. Since different subspaces of the feature space may lead to different partitions of the data set, an efficient algorithm to tackle multi-modal environments is needed. In this context, the Multi-Niche Crowding Genetic Algorithm is used for searching relevant feature subsets. The proposed method is designed to deal with the inherent biases regarding the number of clusters and the number of features that appear in an unsupervised framework. The first one is eliminated with the aid of a new unsupervised clustering criterion, while the second is tackled with the aid of cross-projection normalization. The method delivers a vector of weights which offers a ranking of features in accordance with their relevance to clustering.