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Feature selection has received considerable attentions in various areas as a way to select informative features and to simplify the statistical model through dimensional reduction. In this work, we introduce a novel concept, membership probability of a feature, and propose a novel approach to feature selection for clustering which can find the most optimal candidate features effectively among the...
Feature selection is an important problem for pattern classifier systems. As compared to unsupervised feature selection methods, supervised feature selection approaches have better performance when the given training samples with supervised information are sufficient. However, in reality, usually only a few labeled data are obtained, since obtaining class labels is expensive but many unlabeled data...
Processing applications with a large number of dimensions has been a challenge to the data mining community. Feature selection is an effective dimensionality reduction technique. However, there are only a few methods proposed for feature selection for clustering. In this paper, a new feature selection algorithm for unsupervised learning is introduced. It is based on the assumption that, in absence...
In this paper, we aim to propose an unsupervised feature ranking algorithm for evaluating features using discovered biclusters which are local patterns extracted from a data matrix. The biclusters can be expressed as sub-matrices which are used for scoring relevant features from two aspects, i.e. the interdependence of features and the separability of instances. The features are thereby ranked with...
For many large-scale datasets it is necessary to reduce dimensionality to the point where further exploration and analysis can take place. As a result, it is important to develop techniques for selecting features from large-scale datasets. However this topic has been well studied in supervised learning area, there are only a few methods proposed for feature selection for clustering. In this paper,...
Text clustering techniques were usually used to structure the text documents into topic related groups which can facilitate users to get a comprehensive understanding on corpus or results from information retrieval system. Most of existing text clustering algorithm which derived from traditional formatted data clustering heavily rely on term analysis methods and adopted vector space model (VSM) as...
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