Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Feature selection in unsupervised learning via evolutionary search
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Feature Selection for Clustering - A Filter Solution
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Bagging for Path-Based Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Soft Computing - A Fusion of Foundations, Methodologies and Applications
IEEE Intelligent Systems
The equation for response to selection and its use for prediction
Evolutionary Computation
Consensus unsupervised feature ranking from multiple views
Pattern Recognition Letters
Journal on Image and Video Processing - Color in Image and Video Processing
Unsupervised data pruning for clustering of noisy data
Knowledge-Based Systems
A genetic algorithm with gene rearrangement for K-means clustering
Pattern Recognition
Learning assignment order of instances for the constrained K-means clustering algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Benchmarking a wide spectrum of metaheuristic techniques for the radio network design problem
IEEE Transactions on Evolutionary Computation
Hierarchical clustering ensemble algorithm based association rules
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Clustering ensemble for unsupervised feature selection
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
A unifying criterion for unsupervised clustering and feature selection
Pattern Recognition
A review: accuracy optimization in clustering ensembles using genetic algorithms
Artificial Intelligence Review
Expert Systems with Applications: An International Journal
A new grouping genetic algorithm for clustering problems
Expert Systems with Applications: An International Journal
A New Unsupervised Feature Ranking Method for Gene Expression Data Based on Consensus Affinity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A semi-supervised feature ranking method with ensemble learning
Pattern Recognition Letters
Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem
Applied Soft Computing
DUET: integration of dynamic and static analyses for malware clustering with cluster ensembles
Proceedings of the 29th Annual Computer Security Applications Conference
Feature selection with SVD entropy: Some modification and extension
Information Sciences: an International Journal
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This paper describes a novel feature selection algorithm for unsupervised clustering, that combines the clustering ensembles method and the population based incremental learning algorithm. The main idea of the proposed unsupervised feature selection algorithm is to search for a subset of all features such that the clustering algorithm trained on this feature subset can achieve the most similar clustering solution to the one obtained by an ensemble learning algorithm. In particular, a clustering solution is firstly achieved by a clustering ensembles method, then the population based incremental learning algorithm is adopted to find the feature subset that best fits the obtained clustering solution. One advantage of the proposed unsupervised feature selection algorithm is that it is dimensionality-unbiased. In addition, the proposed unsupervised feature selection algorithm leverages the consensus across multiple clustering solutions. Experimental results on several real data sets demonstrate that the proposed unsupervised feature selection algorithm is often able to obtain a better feature subset when compared with other existing unsupervised feature selection algorithms.