In this research, a systematic study is conducted of four dimension reduction techniques for the text clustering problem, using five benchmark data sets. Of the four methods -- Ind...
Bin Tang, Michael A. Shepherd, Malcolm I. Heywood,...
In this paper we present a method to cluster large datasets that change over time using incremental learning techniques. The approach is based on the dynamic representation of clus...
Abstract. In this paper, we propose a new ant-based clustering algorithm called AntClust. It is inspired from the chemical recognition system of ants. In this system, the continuou...
Previous research has demonstrated the utility of clustering in inducing semantic verb classes from undisambiguated corpus data. We describe a new approach which involves clusteri...
We study a general algorithm to improve accuracy in cluster analysis that employs the James-Stein shrinkage effect in k-means clustering. We shrink the centroids of clusters towar...