• All the entire learning process occurs without supervision.
• Self-Organizing feature map (SOM) refers to a neural network, which is trained using competitive learning. The competition process suggests that, some criteria select a wining processing element. so Self-Organizing feature map is unsupervised neural network.
• Self-Organizing maps are even often referred as Kohonen maps.
• In other words A Self-Organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map, and is therefore a method to do dimensionality reduction.
• Self-Organizing maps (SOMs) can be used for clustering of data because Self-Organizing maps are a data visualization technique which reduce the dimensions of data through the use of self-organizing neural networks. It is a type of unsupervised learning. The goal is to discover some underlying structure of the data.
• Self-Organizing Map (SOM) is an unsupervised neural network that reduces the input dimensionality in order to represent its distribution as a map.
• They are also known as feature maps, as they are basically retraining the features of the input data, and simply grouping themselves as undirected by the similarity between each other.
• Self-organizing maps have two layers, the first one is the input layer and the second one is the output layer or the feature map.
• unlike other ANN (Artificial Neural network) types, SOM doesn't have activation function in neurons, we directly pass weights to output layer without doing anything.
• Reducing the data dimensions are the main goal of Self-Organizing maps.
• Project prioritization and selection
• Seismic facies analysis for oil and gas exploration
• Failure mode and effects analysis
• creation of artwork
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