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Flocking-based document clustering on the graphics processing unit. NICSO (2007)
| Citations: | 4 - 1 self |
BibTeX
@MISC{Charles07flocking-baseddocument,
author = {Jesse St. Charles and Thomas E. Potok and Robert Patton and Xiaohui Cui},
title = {Flocking-based document clustering on the graphics processing unit. NICSO},
year = {2007}
}
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Abstract
Summary. Analyzing and grouping documents by content is a complex problem. One explored method of solving this problem borrows from nature, imitating the flocking behavior of birds. Each bird represents a single document and flies toward other documents that are similar to it. One limitation of this method of document clustering is its complexity O(n 2). As the number of documents grows, it becomes increasingly difficult to receive results in a reasonable amount of time. However, flocking behavior, along with many naturally inspired algorithms such as ant colony optimization and particle swarm optimization, are highly parallel and have found increased performance on expensive cluster computers. In the last few years, the graphics processing unit (GPU) has received attention for its ability to solve highlyparallel and semi-parallel problems much faster than the traditional sequential processor. Some applications see a huge increase in performance on this new platform. The cost of these high-performance devices is also marginal when compared with the price of cluster machines. In this paper, we have conducted research to exploit this architecture and apply its strengths to the document flocking problem. Our
Keyphrases
flocking-based document reasonable amount particle swarm optimization ant colony optimization semi-parallel problem inspired algorithm traditional sequential processor huge increase expensive cluster computer document flocking problem document grows high-performance device document clustering new platform single document last year cluster machine complex problem
