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- W2889156832 abstract "Feature selection is an important data analysis technique that used to reduce the redundancy of features and exploit hidden information in high-dimensional data. In this paper we propose a similarity metric based feature selection method named Fesim. We use the Euclidean distance to measure the similarity among all features, and then apply the density based DBSCAN algorithm to clustering features which to be relevant. Moreover, we present a strategy which choose representative features of each cluster accurately. We conducted comprehensive experiments to evaluate the proposed approach, and the results on different datasets are demonstrated its superiority." @default.
- W2889156832 created "2018-09-07" @default.
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- W2889156832 date "2018-08-30" @default.
- W2889156832 modified "2023-09-27" @default.
- W2889156832 title "Enhancing Feature Selection with Density Cluster for Better Clustering" @default.
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- W2889156832 doi "https://doi.org/10.1007/978-3-030-00211-4_15" @default.
- W2889156832 hasPublicationYear "2018" @default.
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