In many applications of supervised learning, automatic feature clustering is often desirable for a better understanding of the interaction among the various features as well as the interplay between the features and the class labels. In addition, for high dimensional data sets, feature clustering has the potential for improvement in classification accuracy and reduction in computational complex...

There exist many approaches to clustering, but the important issue of feature selection, i.e., selecting the data attributes that are relevant for clustering, is rarely addressed. Feature selection for clustering is difficult due to the absence of class labels. We propose two approaches to feature selection in the context of Gaussian mixture-based clustering. In the first one, instead of making...

Feature selection is a basic step in the construction of a vector space or bag of words model [BB99]. In particular, when the processing task is to partition a given document collection into clusters of similar documents a choice of good features along with good clustering algorithms is of paramount importance. This chapter suggests two techniques for feature or term selection along with a numb...

For most clustering algorithms, their performance will strongly depend on the data representation. In this summary, we briefly introduce our three approaches to obtaining better data representations through feature selection, particularly for the clustering. Empirical studies demonstrate the effectiveness of the proposed methods on several benchmark datasets.

This paper addresses the problem of feature selection for the high dimensional data clustering. This is a difficult problem because the ground truth class labels that can guide the selection are unavailable in clustering. Besides, the data may have a large number of features and the irrelevant ones can ruin the clustering. In this paper, we propose a novel feature weighting scheme for a kernel ...

Within this paper we present a framework for discovering patterns in time-series by unsupervised feature selection and unsupervised, self-organised clustering. The proposed unsupervised feature selection algorithm is determining the feature relevance for a variety of transformations to select a set of features to build the feature space. We propose to take the phase space as basis and extend it...

Due to the absence of class labels, unsupervised feature selection is much more difficult than supervised feature selection. Traditional unsupervised feature selection algorithms usually select features to preserve the structure of the data set. Inspired from the recent developments on discriminative clustering, we propose in this paper a novel unsupervised feature selection approach via Joint ...

Documents clustering become an essential technology with the popularity of the Internet. That also means that fast and high-quality document clustering technique play core topics. Text clustering or shortly clustering is about discovering semantically related groups in an unstructured collection of documents. Clustering has been very popular for a long time because it provides unique ways of di...

In this article we use a combination of neural networks with other techniques for the analysis of orthophotos. Our goal is to obtain results that can serve as a useful groundwork for interactive exploration of the terrain in detail. In our approach we split an aerial photo into a regular grid of segments and for each segment we detect a set of features. These features depict the segment from th...

The World Wide Web (WWW) is a reservoir of enormous amount of data which is primarily embedded within unstructured text documents. E-commerce websites, social networking sites, and discussion forums have become a common place for writing informal opinions about products and other related information. A substantial amount of research has been directed towards mining these texts and concludes on ...