This paper presents the results of a comparative study of filtering methods for feature selection in web document clustering. First, we focused on feature selection methods based on Mutual Information (MI) and Information Gain (IG). With those features and feature values, and using MI and IG, we extracted from documents representative max-value features as well as a representative cluster for a...

Text clustering is unsupervised machine learning method.It needs representation of objects and similarity measure. which compares distribution of features between objects. For the high dimensionality of feature space performance of clustering algorithms decreases.Two techniques are used to deal with this problem: feature extraction and feature selection.In this paper, we describe the hybrid met...

Feature construction is a pre-processing technique to create new features with better discriminating ability from the original features. Genetic programming (GP) has been shown to be a prominent technique for this task. However, applying GP to high-dimensional data is still challenging due to the large search space. Feature clustering groups similar features into clusters, which can be used for...

In previous research in automatic verb classification, syntactic features have proved the most useful features, although manual classifications rely heavily on semantic features. We show, in contrast with previous work, that considerable additional improvement can be obtained by using semantic features in automatic classification: verb selectional preferences acquired from corpus data using a f...

DNA microarray technologies have made it possible to analyze simultaneously thousands of gene expression patterns. This paper presents the successful application of a new clustering algorithm to gene expression data involving 72 data points in a 6817 dimensional space. We compare the new clustering algorithm to other clustering algorithms that have been used for expression analysis and show its...

Many datasets include feature values that are missing but may be acquired at a cost. In this paper, we consider the clustering task for such datasets, and address the problem of acquiring missing feature values that improve clustering quality in a cost-effective manner. Since acquiring all missing information may be unnecessarily expensive, we propose a framework for iteratively selecting featu...

Many datasets include feature values that are missing but may be acquired at a cost. In this paper, we consider the clustering task for such datasets, and address the problem of acquiring missing feature values that improve clustering quality in a cost-effective manner. Since acquiring all missing information may be unnecessarily expensive, we propose a framework for iteratively selecting featu...

The goal of unsupervised learning, i.e., clustering, is to determine the intrinsic structure of unlabeled data. Feature selection for clustering improves the performance of grouping by removing irrelevant features. Typical feature selection algorithms select a common feature subset for all the clusters. Consequently, clusters embedded in different feature subspaces are not able to be identified...