Data Science: Self-Organizing Map (Definitions)

"A clustering neural net, with topological structure among cluster units." (Laurene V Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications", 1994)

"A self organizing map is a form of Kohonen network that arranges its clusters in a (usually) two-dimensional grid so that the codebook vectors (the cluster centers) that are close to each other on the grid are also close in the k-dimensional feature space. The converse is not necessarily true, as codebook vectors that are close in feature-space might not be close on the grid. The map is similar in concept to the maps produced by descriptive techniques such as multi-dimensional scaling (MDS)." (William J Raynor Jr., "The International Dictionary of Artificial Intelligence", 1999)

"result of a nonparametric regression process that is mainly used to represent high-dimensional, nonlinearly related data items in an illustrative, often two-dimensional display, and to perform unsupervised classification and clustering." (Teuvo Kohonen, "Self-Organizing Maps" 3rd Ed., 2001)

"a method of organizing and displaying textual information according to the frequency of occurrence of text and the relationship of text from one document to another." (William H Inmon, "Building the Data Warehouse", 2005)

"A type of unsupervised neural network used to group similar cases in a sample. SOMs are unsupervised (see supervised network) in that they do not require a known dependent variable. They are typically used for exploratory analysis and to reduce dimensionality as an aid to interpretation of complex data. SOMs are similar in purpose to Ic-means clustering and factor analysis." (David Scarborough & Mark J Somers, "Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior", 2006)

"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." (Atiq Islam et al, "CNS Tumor Prediction Using Gene Expression Data Part II", Encyclopedia of Artificial Intelligence, 2009)

"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." (Emmanuel Udoh & Salim Bhuiyan, "C-MICRA: A Tool for Clustering Microarray Data", 2009)

"A neural network unsupervised method of vector quantization widely used in classification. Self-Organizing Maps are a much appreciated for their topology preservation property and their associated data representation system. These two additive properties come from a pre-defined organization of the network that is at the same time a support for the topology learning and its representation. (Patrick Rousset & Jean-Francois Giret, "A Longitudinal Analysis of Labour Market Data with SOM" Encyclopedia of Artificial Intelligence, 2009)

"A simulated neural network based on a grid of artificial neurons by means of prototype vectors. In an unsupervised training the prototype vectors are adapted to match input vectors in a training set. After completing this training the SOM provides a generalized K-means clustering as well as topological order of neurons." (Laurence Mukankusi et al, "Relationships between Wireless Technology Investment and Organizational Performance", 2009)

"A subtype of artificial neural network. It is trained using unsupervised learning to produce low dimensional representation of the training samples while preserving the topological properties of the input space." (Soledad Delgado et al, "Growing Self-Organizing Maps for Data Analysis", 2009)

"An unsupervised neural network providing a topology-preserving mapping from a high-dimensional input space onto a two-dimensional output space." (Thomas Lidy & Andreas Rauber, "Music Information Retrieval", 2009)

"Category of algorithms based on artificial neural networks that searches, by means of self-organization, to create a map of characteristics that represents the involved samples in a determined problem." (Paulo E Ambrósio, "Artificial Intelligence in Computer-Aided Diagnosis", 2009)

"Self-organizing maps (SOMs) are a data visualization technique which reduce the dimensions of data through the use of self-organizing neural networks." (Lluís Formiga & Francesc Alías, "GTM User Modeling for aIGA Weight Tuning in TTS Synthesis", Encyclopedia of Artificial Intelligence, 2009)

"SOFM [self-organizing feature map] is a data mining method used for unsupervised learning. The architecture consists of an input layer and an output layer. By adjusting the weights of the connections between input and output layer nodes, this method identifies clusters in the data." (Indranil Bose, "Data Mining in Tourism", 2009)

"The self-organizing map is a subtype of artificial neural networks. It is trained using unsupervised learning to produce low dimensional representation of the training samples while preserving the topological properties of the input space. The self-organizing map is a single layer feed-forward network where the output syntaxes are arranged in low dimensional (usually 2D or 3D) grid. Each input is connected to all output neurons. Attached to every neuron there is a weight vector with the same dimensionality as the input vectors. The number of input dimensions is usually a lot higher than the output grid dimension. SOMs are mainly used for dimensionality reduction rather than expansion." (Larbi Esmahi et al, "Adaptive Neuro-Fuzzy Systems", Encyclopedia of Artificial Intelligence, 2009)

"A type of neural network that uses unsupervised learning to produce two-dimensional representations of an input space." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The Self-organizing map is a non-parametric and non-linear neural network that explores data using unsupervised learning. The SOM can produce output that maps multidimensional data onto a two-dimensional topological map. Moreover, since the SOM requires little a priori knowledge of the data, it is an extremely useful tool for exploratory analyses. Thus, the SOM is an ideal visualization tool for analyzing complex time-series data." (Peter Sarlin, "Visualizing Indicators of Debt Crises in a Lower Dimension: A Self-Organizing Maps Approach", 2012)

"SOMs or Kohonen networks have a grid topology, with unequal grid weights. The topology of the grid provides a low dimensional visualization of the data distribution." (Siddhartha Bhattacharjee et al, "Quantum Backpropagation Neural Network Approach for Modeling of Phenol Adsorption from Aqueous Solution by Orange Peel Ash", 2013)

"An unsupervised neural network widely used in exploratory data analysis and to visualize multivariate object relationships." (Manuel Martín-Merino, "Semi-Supervised Dimension Reduction Techniques to Discover Term Relationships", 2015)

"ANN used for visualizing low-dimensional views of high-dimensional data." (Pablo Escandell-Montero et al, "Artificial Neural Networks in Physical Therapy", 2015)

"Is a unsupervised learning ANN, which means that no human intervention is needed during the learning and that little needs to be known about the characteristics of the input data." (Nuno Pombo et al, "Machine Learning Approaches to Automated Medical Decision Support Systems", 2015)

"A kind of artificial neural network which attempts to mimic brain functions to provide learning and pattern recognition techniques. SOM have the ability to extract patterns from large datasets without explicitly understanding the underlying relationships. They transform nonlinear relations among high dimensional data into simple geometric connections among their image points on a low-dimensional display." (Felix Lopez-Iturriaga & Iván Pastor-Sanz, "Using Self Organizing Maps for Banking Oversight: The Case of Spanish Savings Banks", 2016)

"Neural network which simulated some cerebral functions in elaborating visual information. It is usually used to classify a large amount of data." (Gaetano B Ronsivalle & Arianna Boldi, "Artificial Intelligence Applied: Six Actual Projects in Big Organizations", 2019)

"Classification technique based on unsupervised-learning artificial neural networks allowing to group data into clusters." Julián Sierra-Pérez & Joham Alvarez-Montoya, "Strain Field Pattern Recognition for Structural Health Monitoring Applications", 2020)

"It is a type of artificial neural network (ANN) trained using unsupervised learning for dimensionality reduction by discretized representation of the input space of the training samples called as map." (Dinesh Bhatia et al, "A Novel Artificial Intelligence Technique for Analysis of Real-Time Electro-Cardiogram Signal for the Prediction of Early Cardiac Ailment Onset", 2020)

"Being a particular type of ANNs, the Self Organizing Map is a simple mapping from inputs: attributes directly to outputs: clusters by the algorithm of unsupervised learning. SOM is a clustering and visualization technique in exploratory data analysis." (Yuh-Wen Chen, "Social Network Analysis: Self-Organizing Map and WINGS by Multiple-Criteria Decision Making", 2021)