The comparison of neural networks abilities for cluster analysis purpose

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F09%3A00504919" target="_blank" >RIV/49777513:23220/09:00504919 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

čeština

Original language name

Porovnání schopností různých druhů neuronových sítí pro účely shlukové analýzy

Original language description

For cluster analysis purpose we can use several types of artificial neural networks. We can choose self-organizing networks or networks with teacher. Neural networks with teacher need the data set with correct pre-classification for the training phase. After the training process these networks are able to classify by teaching class. Self-organizing networks (known as the networks with competitive learning) are able to self-classify and make clusters, identifying clustering data. This principle is in fact similar to classic statistical cluster analysis method. This article is focused to all kinds of neural networks, which are proper for purpose of cluster analysis (multi-layer perceptron networks, RBF networks, linear networks and networks with competitive learning) and appreciate the possibilities for use to both cluster analysis methods.

Czech name

Porovnání schopností různých druhů neuronových sítí pro účely shlukové analýzy

Czech description

For cluster analysis purpose we can use several types of artificial neural networks. We can choose self-organizing networks or networks with teacher. Neural networks with teacher need the data set with correct pre-classification for the training phase. After the training process these networks are able to classify by teaching class. Self-organizing networks (known as the networks with competitive learning) are able to self-classify and make clusters, identifying clustering data. This principle is in fact similar to classic statistical cluster analysis method. This article is focused to all kinds of neural networks, which are proper for purpose of cluster analysis (multi-layer perceptron networks, RBF networks, linear networks and networks with competitive learning) and appreciate the possibilities for use to both cluster analysis methods.

Type

O - Miscellaneous

CEP classification

JB - Sensors, detecting elements, measurement and regulation

OECD FORD branch

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Project

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Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2009

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů