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Authors: Gerasimos Spanakis and Gerhard Weiss

Affiliation: Maastricht University, Netherlands

ISBN: 978-989-758-172-4

Keyword(s): Self-Organizing Maps, Clustering, Visualization, Unsupervised Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Bioinformatics ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods ; Visualization

Abstract: Self-Organizing Map (SOM) is a neural network model which is used to obtain a topology-preserving mapping from the (usually high dimensional) input/feature space to an output/map space of fewer dimensions (usually two or three in order to facilitate visualization). Neurons in the output space are connected with each other but this structure remains fixed throughout training and learning is achieved through the updating of neuron reference vectors in feature space. Despite the fact that growing variants of SOM overcome the fixed structure limitation they increase computational cost and also do not allow the removal of a neuron after its introduction. In this paper, a variant of SOM is proposed called AMSOM (Adaptive Moving Self-Organizing Map) that on the one hand creates a more flexible structure where neuron positions are dynamically altered during training and on the other hand tackles the drawback of having a predefined grid by allowing neuron addition and/or removal during trainin g. Experiments using multiple literature datasets show that the proposed method improves training performance of SOM, leads to a better visualization of the input dataset and provides a framework for determining the optimal number and structure of neurons. (More)

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Paper citation in several formats:
Spanakis, G. and Weiss, G. (2016). AMSOM: Adaptive Moving Self-organizing Map for Clustering and Visualization.In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-172-4, pages 129-140. DOI: 10.5220/0005704801290140

@conference{icaart16,
author={Gerasimos Spanakis. and Gerhard Weiss.},
title={AMSOM: Adaptive Moving Self-organizing Map for Clustering and Visualization},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2016},
pages={129-140},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005704801290140},
isbn={978-989-758-172-4},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - AMSOM: Adaptive Moving Self-organizing Map for Clustering and Visualization
SN - 978-989-758-172-4
AU - Spanakis, G.
AU - Weiss, G.
PY - 2016
SP - 129
EP - 140
DO - 10.5220/0005704801290140

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