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Experts from various sectors, utilize data mining techniques to discover most useful information from the huge amount of data, to improve their quality of outcomes. The Presence of irrelevant and redundant features affects the accuracy of mining result. Before applying any mining technique, the data need to be preprocessed. Feature selection, a preprocessing step in data mining provides better mining performance. In this paper, we propose a new two step algorithm for unsupervised feature selection. In the first step Laplacian Score is used to select the important features. And in the second step, Symmetric Uncertainty is used to remove redundant features. The experimental results show that the proposed algorithm outperforms the Laplacian Score algorithm.

An Extended Laplacian Score Algorithm for Unsupervised Feature Selection