1. Self organizing networks.

2. What is Clustering?
Find K clusters (or a classification that consists of K clusters) so that the objects of one cluster are similar to each other whereas objects of different clusters are dissimilar.

3. Clustering can be considered the most important unsupervised learning problem; it deals with finding a structure in a collection of unlabeled data.
A loose definition of clustering could be “the process of organizing objects into groups whose members are similar in some way”. A cluster is therefore a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters.

4. Examples of Clustering Applications
Marketing: Help marketers discover distinct groups in their customer bases, and then use this knowledge to develop targeted marketing programs
Land use: Identification of areas of similar land use in an earth observation database
Insurance: Identifying groups of motor insurance policy holders with a high average claim cost
City-planning: Identifying groups of houses according to their house type, value, and geographical location
Earth-quake studies: Observed earth quake epicenters should be clustered along continent faults

5. Stages in clustering

6. A self-organizing map (SOM) or self-organising 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.

7. Self-organizing maps are different from other artificial neural networks as they apply competitive learning as opposed to error-correction learning (such as backpropagation with gradient descent), and in the sense that they use a neighborhood function to preserve the topological properties of the input space

8. Where Self-organizing map (SOM) network is commonly used network for clustering have competitive layer consist of neurons which group similar kind of data into classes. Features extracted are provided as input to input layer

9. Note :-In neural network, Feature Map means map your input features to hidden units to form new features to feed to the next layer.
A feature map is a function which maps a data vector to feature space. The main logic in machine learning for doing so is to present your learning algorithm with data that it is better able to regress or classify.