Scaling the data mining step in knowledge discovery using oceanographic data

Authors:
Bruce Wooley;Susan Bridges;Julia Hodges;Anthony Skjellum
Affiliations:
-;-;-;-
Venue:
IEA/AIE '00 Proceedings of the 13th international conference on Industrial and engineering applications of artificial intelligence and expert systems: Intelligent problem solving: methodologies and approaches
Year:
2000

Citing 6
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MPI: The Complete Reference

MPI: The Complete Reference
Knowledge Discovery in an Oceanographic Database

Applied Intelligence

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Abstract

Knowledge discovery from large acoustic images is a computationally intensive task. The data-mining step in the knowledge discovery process that involves unsupervised learning (clustering) consumes the bulk of the computation. We have developed a technique that allows us to partition the data, distribute it to different processors for training, and train a single system to join the results of the independent categorizers. We report preliminary results using this approach for knowledge discovery with large acoustic images having more than 10, 000 training instances.