Fast and flexible unsupervised custering algorithm based on ultrametric properties
Proceedings of the 7th ACM symposium on QoS and security for wireless and mobile networks
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High-dimensional data clustering is an open problem in modern data mining. This paper proposed a new genetic algorithm-based feature selection for high-dimensional data clustering, called GA-FSFclustering. This approach searches effective feature subsets for clustering in all features by genetic algorithm. The candidate features and cluster centers are real number encoded. A new criterion for evaluating feature subsets is employed as the fitness function. The experimental results indicate the feasibility and efficiency of the GA-FSFclustering algorithm.