On Cluster Analysis Via Neuron Proximity in Monitored Self-Organizing Maps

Susana Vegas-Azcárate, Jorge Muruzábal

Abstract

A potential application of self-organizing or topographic maps is clustering and visualization of high-dimensional data. It is well-known that an appropriate choice of the degree of smoothness in topographic maps is crucial for obtaining sensible results. Indeed, experimental evidence suggests that suitably monitored topographic maps should be preferred as they lead to more accurate performance. This paper reconsiders the basic toolkit for cluster analysis —based on the relative distance from each pointer to its immediate neighbours on the network— from this monitoring perspective. It is shown that the idea works nicely, that is, much useful information can be encoded and recovered via the trained map alone (ignoring any possible density estimate available). Moreover, the fact that a topographic map is not restricted to metric vector spaces makes this learning structure a perfect tool to deal with biological data, such as DNA or protein sequences of living organisms, for which only a similarity measure is readily available.

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Paper Citation


in Harvard Style

Vegas-Azcárate S. and Muruzábal J. (2005). On Cluster Analysis Via Neuron Proximity in Monitored Self-Organizing Maps . In Proceedings of the 1st International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2005) ISBN 972-8865-35-X, pages 50-59. DOI: 10.5220/0001192800500059


in Bibtex Style

@conference{bpc05,
author={Susana Vegas-Azcárate and Jorge Muruzábal},
title={On Cluster Analysis Via Neuron Proximity in Monitored Self-Organizing Maps},
booktitle={Proceedings of the 1st International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2005)},
year={2005},
pages={50-59},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001192800500059},
isbn={972-8865-35-X},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2005)
TI - On Cluster Analysis Via Neuron Proximity in Monitored Self-Organizing Maps
SN - 972-8865-35-X
AU - Vegas-Azcárate S.
AU - Muruzábal J.
PY - 2005
SP - 50
EP - 59
DO - 10.5220/0001192800500059