Self-Organizing Maps

The Self-Organizing Map (SOM) is a neural network algorithm, which uses a competitive learning technique to train itself in an unsupervised manner. SOMs are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of th...

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collection Directory of Open Access Books
description The Self-Organizing Map (SOM) is a neural network algorithm, which uses a competitive learning technique to train itself in an unsupervised manner. SOMs are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space and they have been used to create an ordered representation of multi-dimensional data which simplifies complexity and reveals meaningful relationships. Prof. T. Kohonen in the early 1980s first established the relevant theory and explored possible applications of SOMs. Since then, a number of theoretical and practical applications of SOMs have been reported including clustering, prediction, data representation, classification, visualization, etc. This book was prompted by the desire to bring together some of the more recent theoretical and practical developments on SOMs and to provide the background for future developments in promising directions. The book comprises of 25 Chapters which can be categorized into three broad areas: methodology, visualization and practical applications.
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spelling doab-20.500.12854ir-648192024-04-14T10:27:49Z Self-Organizing Maps K Matsopoulos, George Data mining thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining The Self-Organizing Map (SOM) is a neural network algorithm, which uses a competitive learning technique to train itself in an unsupervised manner. SOMs are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space and they have been used to create an ordered representation of multi-dimensional data which simplifies complexity and reveals meaningful relationships. Prof. T. Kohonen in the early 1980s first established the relevant theory and explored possible applications of SOMs. Since then, a number of theoretical and practical applications of SOMs have been reported including clustering, prediction, data representation, classification, visualization, etc. This book was prompted by the desire to bring together some of the more recent theoretical and practical developments on SOMs and to provide the background for future developments in promising directions. The book comprises of 25 Chapters which can be categorized into three broad areas: methodology, visualization and practical applications. 2021-04-20T14:56:17Z 2021-04-20T14:56:17Z 2010 book ONIX_20210420_9789533070742_175 9789533070742 9789535159001 https://directory.doabooks.org/handle/20.500.12854/64819 eng image/jpeg n/a https://www.intechopen.com/books https://mts.intechopen.com/storage/books/3176/authors_book/authors_book.pdf IntechOpen IntechOpen 10.5772/3473 10.5772/3473 78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6 9789533070742 9789535159001 IntechOpen 432 open access
spellingShingle Data mining
thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining
Self-Organizing Maps
title Self-Organizing Maps
title_full Self-Organizing Maps
title_fullStr Self-Organizing Maps
title_full_unstemmed Self-Organizing Maps
title_short Self-Organizing Maps
title_sort self organizing maps
topic Data mining
thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining
topic_facet Data mining
thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining
url ONIX_20210420_9789533070742_175