In semi-supervised clustering, domain knowledge can be converted to constraints and used to guide the clustering. In this paper we propose a feature selection algorithm for semi-s...
In this paper we propose and test the use of hierarchical clustering for feature selection. The clustering method is Ward's with a distance measure based on GoodmanKruskal ta...
Background: Clustering is one of the most commonly used methods for discovering hidden structure in microarray gene expression data. Most current methods for clustering samples ar...
As an important technique for data analysis, clustering has been employed in many applications such as image segmentation, document clustering and vector quantization. Divisive cl...
Word Sense Disambiguation in text is still a difficult problem as the best supervised methods require laborious and costly manual preparation of training data. Thus, this work focu...