Practical clustering algorithms require multiple data scans to achieve convergence. For large databases, these scans become prohibitively expensive. We present a scalable clusteri...
We revisit and use the dependence transformation method to generate parallel algorithms suitable for cluster and grid computing. We illustrate this method in two applications: to o...
Ulisses Kendi Hayashida, Kunio Okuda, Jairo Panett...
Text clustering is an established technique for improving quality in information retrieval, for both centralized and distributed environments. However, for highly distributed envir...
This paper explores the challenge of scaling up language processing algorithms to increasingly large datasets. While cluster computing has been available in commercial environment...
In distributed data mining models, adopting a flat node distribution model can affect scalability. To address the problem of modularity, flexibility and scalability, we propose...