A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Weighted cluster ensembles: Methods and analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
An efficient optimization method for revealing local optima of projection pursuit indices
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Optimized ensembles for clustering noisy data
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
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The current paper presents a framework for linear feature extraction applicable in both unsupervised and supervised data analysis, as well as in their hybrid - the semi-supervised scenario. New features are extracted in a filter manner with a multi-modal genetic algorithm that optimizes simultaneously several projection indices. Experimental results show that the new algorithm is able to provide a compact and improved representation of the data set. The use of mixed labeled and unlabeled data under this scenario improves considerably the performance of constrained clustering algorithms such as constrained k-Means.