The recent years have witnessed a surge of interests of semi-supervised clustering methods, which aim to cluster the data set under the guidance of some supervisory information. U...
Clusters of workstations are becoming popular platforms for parallel computing, but performance on these systems is more complex and harder to predict than on traditional parallel...
Geetanjali Sampemane, Scott Pakin, Andrew A. Chien
Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space. Despite significant research, these methods have re...
Clustering is an essential data mining task with various types of applications. Traditional clustering algorithms are based on a vector space model representation. A relational dat...
Ontology learning integrates many complementary techniques, including machine learning, natural language processing, and data mining. Specifically, clustering techniques facilitat...