Feature subset selection is a major problem in data mining which can help to reduce computation time, improve prediction performance, and build understandable models. Specifically, feature selection realized in the absence of class labels, namely unsupervised feature selection, is challenging and interesting. In this paper a novel graph-theoretic approach for unsupervised feature selection has been proposed. The proposed method works in three steps. In the first step, the entire feature set is represented as a weighted graph. In the second step, the features are divided into several clusters using a community detection algorithm and finally in the third step, a novel iterative search strategy based on node centrality is developed to select the final subset of features. The proposed feature selection method offers two major advantages: first, our method groups features into different clusters based on their similarities, in which the features in the same cluster are similar to each other, and to obtain the reduced redundancy set, the final subset of features is selected from different clusters. Second, the node centrality measure and term variance are used to identify the most representative and informative feature subset; hence, the optimal size of the feature subset can be automatically determined. The performance of the proposed method has been compared to those of the state-of-the-art unsupervised and supervised feature selection methods on eight benchmark classification problems. The results show that our method has produced consistently better classification accuracies.
