Abstract. In this paper we introduce a general framework for hierarchical clustering that deals with both static and dynamic data sets. From this framework, different hierarchical...
The k-means algorithm is widely used for clustering because of its computational efficiency. Given n points in d-dimensional space and the number of desired clusters k, k-means see...
When users' tasks in a distributed heterogeneous computing environment (e.g., cluster of heterogeneous computers) are allocated resources, the total demand placed on some sys...
Jong-Kook Kim, Debra A. Hensgen, Taylor Kidd, Howa...
Clustered microarchitectures are an effective organization to deal with the problem of wire delays and complexity by partitioning some of the processor resources. The organization ...
We present a powerful meta-clustering technique called Iterative Double Clustering (IDC). The IDC method is a natural extension of the recent Double Clustering (DC) method of Slon...