This paper investigates the design of parallel algorithmic strategies that address the efficient use of both, memory hierarchies within each processor and a multilevel clustered ...
Frank K. H. A. Dehne, Stefano Mardegan, Andrea Pie...
Efficient and effective analysis of large datasets from microarray gene expression data is one of the keys to time-critical personalized medicine. The issue we address here is the ...
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
Abstract. In this paper we present a novel analysis of a random sampling approach for three clustering problems in metric spaces: k-median, min-sum kclustering, and balanced k-medi...
Several clustering algorithms equipped with pairwise hard constraints between data points are known to improve the accuracy of clustering solutions. We develop a new clustering alg...
Martin H. C. Law, Alexander P. Topchy, Anil K. Jai...