In this paper, we consider the clustering of resources on large scale platforms. More precisely, we target parallel applications consisting of independant tasks, where each task is...
Olivier Beaumont, Nicolas Bonichon, Philippe Ducho...
Abstract. Clustering is a widely used unsupervised data analysis technique in machine learning. However, a common requirement amongst many existing clustering methods is that all p...
Random Indexing K-tree is the combination of two algorithms suited for large scale document clustering. Keywords Random Indexing, K-tree, Dimensionality Reduction, B-tree, Search T...
Christopher M. De Vries, Lance De Vine, Shlomo Gev...
—Normalization before clustering is often needed for proximity indices, such as Euclidian distance, which are sensitive to differences in the magnitude or scales of the attribute...
In this paper, we propose GAD (General Activity Detection) for fast clustering on large scale data. Within this framework we design a set of algorithms for different scenarios: (...
Jiawei Han, Liangliang Cao, Sangkyum Kim, Xin Jin,...