Abstract. Most correlation clustering algorithms rely on principal component analysis (PCA) as a correlation analysis tool. The correlation of each cluster is learned by applying P...
Linear Discriminant Analysis (LDA) has been a popular method for extracting features that preserves class separability. The projection functions of LDA are commonly obtained by max...
Analysis techniques, such as control-flow, data-flow, and control-dependence, are used for a variety of maintenance tasks, including regression testing, dynamic execution profilin...
In this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fMRI. In particular, we consider the K-Means and Spectral Clustering...
Archana Venkataraman, Koene R. A. Van Dijk, Randy ...
How can we efficiently find a clustering, i.e. a concise description of the cluster structure, of a given data set which contains an unknown number of clusters of different shape ...