Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Self-Organizing Maps
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Visual Explorations in Finance
Visual Explorations in Finance
High-Performance Programming Support for Multimedia Document DataBase Management
HPCN Europe '99 Proceedings of the 7th International Conference on High-Performance Computing and Networking
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The self-organizing map is a prominent unsupervised neural network model which lends itself to the analysis of high-dimensional input data. However, the high execution times required to train the map put a limit to its application in many high-performance data analysis application domains, where either very large datasets are encountered and/or interactive response times are required. In this paper we present the parSOM, a software-based parallel implementation of the self-organizing map, which is particularly optimized for the analysis of high-dimensional input data. This model scales well in a parallel execution environment, and, by coping with memory latencies, a better than linear speed-up can be achieved using a simple, asymmetric model of parallelization. We demonstrate the benefits of the proposed implementation in the field of text classification, which due to the high dimensionalities of the data spaces encountered, forms a prominent application domain for high-performance computing.