Artificial Intelligence Review - Special issue on lazy learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Clustering Validation Techniques
Journal of Intelligent Information Systems
A Coevolutionary Approach to Learning Sequential Decision Rules
Proceedings of the 6th International Conference on Genetic Algorithms
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Examining Locally Varying Weights for Nearest Neighbor Algorithms
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Symbiotic Coevolution of Artificial Neural Networks and Training Data Sets
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Cluster validation techniques for genome expression data
Signal Processing - Special issue: Genomic signal processing
An Unsupervised Collaborative Learning Method to Refine Classification Hierarchies
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Weighting Unusual Feature Types
Weighting Unusual Feature Types
Resampling Method for Unsupervised Estimation of Cluster Validity
Neural Computation
Comparison between two coevolutionary feature weighting algorithms in clustering
Pattern Recognition
A clustering framework based on subjective and objective validity criteria
ACM Transactions on Knowledge Discovery from Data (TKDD)
Exploitation of a parallel clustering algorithm on commodity hardware with P2P-MPI
The Journal of Supercomputing
Expert Systems with Applications: An International Journal
From variable weighting to cluster characterization in topographic unsupervised learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Weighted instance-based learning using representative intervals
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
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This paper presents a new process for modular clustering of complex data, like remote sensing images. This method performs feature weighting in a wrapper approach. The proposed method is a modular clustering method that combines several extractors, which are local specialists, each one extracting one cluster only and using different feature weights. A new clustering quality criterion, adapted to independent clusters, is defined. The weight learning is performed through a cooperative coevolution algorithm, where each species represents one of the clusters to be extracted. A set of extracted clusters forms a partial soft clustering but can be transformed in a classic hard clustering. Some tests, on datasets from the UCI repository and on hyperspectral remote sensing image, have been performed and show the validity of the approach.