Hachemi Bennaceur, Meznah Almutairy, Norah Alhussain*
Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2687-2706, 2023, DOI:10.32604/iasc.2023.038723
Abstract The dimensionality of data is increasing very rapidly, which creates challenges for most of the current mining and learning algorithms, such as large memory requirements and high computational costs. The literature includes much research on feature selection for supervised learning. However, feature selection for unsupervised learning has only recently been studied. Finding the subset of features in unsupervised learning that enhances the performance is challenging since the clusters are indeterminate. This work proposes a hybrid technique for unsupervised feature selection called GAk-MEANS, which combines the genetic algorithm (GA) approach with the classical k-Means algorithm. In the proposed algorithm, a new… More >
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