Feature selection has improved the performance of text clustering. Global feature selection tries to identify a single subset of features which are relevant to all clusters. However, the clustering process might be improved by considering different subsets of features for locally describing each cluster. In this work, we introduce the method ZOOM-IN to perform local feature selection for partit...

Feature-weight assignment can be regarded as a generalization of feature selection. That is, if all values of featureweights are either 1 or 0, feature-weight assignment degenerates to the special case of feature selection. Generally speaking, a number in 1⁄20; 1 can be assigned to a feature for indicating the importance of the feature. This paper shows that an appropriate assignment of feature...

The explosive growth of information stored in unstructured texts created a great demand for new and powerful tools to acquire useful information, such as text mining. Document clustering is one of its the powerful methods and by which document retrieval, organization and summarization can be achieved. Text documents are the unstructured databases that contain raw data collection. The clustering...

In this paper, an unsupervised segmentation method using clustering is presented for color images. We propose to use a neural network based approach to automatic feature selection to achieve adaptive segmentation of color images. With a self-organizing feature map (SOFM), multiple color features can be analyzed, and the useful feature sequence (feature vector) can then be determined. The encode...

This paper explores the problem of feature subset selection for unsupervised learning within the wrapper framework. In particular, we examine feature subset selection wrapped around expectation-maximization (EM) clustering with order identiication (identifying the number of clusters in the data). We investigate two diierent performance criteria for evaluating candidate feature subsets: scatter ...