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What is Self-Organizing Maps (SOMs)

Chapter 48
Encyclopedia of Artificial Intelligence
A method to learn to cluster input vectors according to how they are naturally grouped in the input space. In its simplest form, the map consists of a regular grid of units and the units learn to represent statistical data described by model vectors. Each map unit contains a vector used to represent the data. During the training process, the model vectors are changed gradually and then the map forms an ordered non-linear regression of the model vectors into the data space.
Published in Chapter:
CNS Tumor Prediction Using Gene Expression Data Part II
Atiq Islam (University of Memphis, USA), Khan M. Iftekharuddin (University of Memphis, USA), E. Olusegun George (University of Memphis, USA), and David J. Russomanno (University of Memphis, USA)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-59904-849-9.ch048
Abstract
In this chapter, we propose a novel algorithm for characterizing a variety of CNS tumors. The proposed algorithm is illustrated with an analysis of an Affymetrix gene expression data from CNS tumor samples (Pomeroy et al., 2002). As discussed in the previous chapter entitled: CNS Tumor Prediction Using Gene Expression Data Part I, we used an ANOVA model to normalize the microarray gene expression measurements. In this chapter, we introduce a systemic way of building tumor prototypes to facilitate automatic prediction of CNS tumors.
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Chapter 94
Validation of Damage Identification Using Non-Linear Data-Driven Modelling
The group of Artificial Neural Networks (ANN) and can be described as a nonlinear, ordered, smooth mapping of high-dimensional input data on the elements of a regular, low-dimensional display. They use an unsupervised algorithm and are also known as Kohonen Maps (see Kohonen, 2001 ).
Published in Chapter: Validation of Damage Identification Using Non-Linear Data-Driven Modelling; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 93
Methodologies of Damage Identification Using Non-Linear Data-Driven Modelling
Belong to the group of Artificial Neural Networks (ANN) and can be described as a nonlinear, ordered, smooth mapping of high-dimensional input data on the elements of a regular, low-dimensional display. They use an unsupervised algorithm and are also known as Kohonen Maps.
Published in Chapter: Methodologies of Damage Identification Using Non-Linear Data-Driven Modelling; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 61
C-MICRA: A Tool for Clustering Microarray Data
A neural-network method that reduces the dimensions of data while preserving the topological properties of the input data. SOM is suitable for visualizing high-dimensional data such as microarray data.
Published in Chapter: C-MICRA: A Tool for Clustering Microarray Data; From: Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Trends
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