Abstract: The problem of feature selection has long been an active research
topic within statistics and pattern recognition. So far, most methods of
feature selection focus on supervised data where class information is
available. For unsupervised data, the related methods of feature selection are
few. The presented article demonstrates a way of unsupervised feature
selection, which is a two-level filter model removing the redundant and
irrelevant features, respectively. The redundant features are eliminated using
any clustering algorithm, and a new method is proposed to remove the irrelevant
features: first rank the features according to their relevance to cluster and
then a subset of relevant features is selected using the Fuzzy Feature
Evaluation Index (FFEI) with some changes and extensions. The experimental
results have shown the effectiveness of the proposed method for
high-dimensional data. Our major contributions are: (1) to present a new
hierarchical filter method for unsupervised feature selection; (2) to propose a
new algorithm for removing the irrelevant features; (3) to extend the FFEI, and
present a method for calculating the approximate weight of feature in FFEI,
which improves the efficiency and robustness of the method.
