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Authors: Alexander Adam ; Sebastian Leuoth ; Sascha Dienelt and Wolfgang Benn

Affiliation: Chemnitz University of Technology, Germany

ISBN: 978-989-8425-05-8

Keyword(s): Neural net, Growing neural gas, Parallelization.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial Applications of Artificial Intelligence ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: The amount of data in databases is increasing steadily. Clustering this data is one of the common tasks in Knowledge Discovery in Databases (KDD). For KDD purposes, this means that many algorithms need so much time, that they become practically unusable. To counteract this development, we try parallelization techniques on that clustering. Recently, new parallel architectures have become affordable to the common user. We investigated especially the GPU (Graphics Processing Unit) and multi-core CPU architectures. These incorporate a huge amount of computing units paired with low latencies and huge bandwidths between them. In this paper we present the results of different parallelization approaches to the GNG clustering algorithm. This algorithm is beneficial as it is an unsupervised learning method and chooses the number of neurons needed to represent the clusters on its own.

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Paper citation in several formats:
Adam A.; Leuoth S.; Dienelt S.; Benn W. and (2010). PERFORMANCE GAIN FOR CLUSTERING WITH GROWING NEURAL GAS USING PARALLELIZATION METHODS.In Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8425-05-8, pages 264-269. DOI: 10.5220/0002903502640269

@conference{iceis10,
author={Alexander Adam and Sebastian Leuoth and Sascha Dienelt and Wolfgang Benn},
title={PERFORMANCE GAIN FOR CLUSTERING WITH GROWING NEURAL GAS USING PARALLELIZATION METHODS},
booktitle={Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2010},
pages={264-269},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002903502640269},
isbn={978-989-8425-05-8},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - PERFORMANCE GAIN FOR CLUSTERING WITH GROWING NEURAL GAS USING PARALLELIZATION METHODS
SN - 978-989-8425-05-8
AU - Adam, A.
AU - Leuoth, S.
AU - Dienelt, S.
AU - Benn, W.
PY - 2010
SP - 264
EP - 269
DO - 10.5220/0002903502640269

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