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Clustering of self organizing map

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  1. Introduction
    1. Neural network
    2. Application and neural network
    3. Supervised and unsupervised learning
    4. Self organizing map
    5. Clustering
    6. Types of clustering
  2. Required analysis and specification
    1. Introduction
    2. Feasibility
    3. Preparation of SRS
  3. Software configuration and requirement
  4. Function oriented software design
    1. Structure analysis and DFD
  5. Coding and testing
    1. Introduction
    2. Code review
    3. Types of testing
  6. Implementation
  7. Conclusion
  8. References

This project is based on the research work on the clustering of Self Organizing Map. SOM is an excellent tool, which is utilized to visualize the properties of data. The number of SOM units is larger than it is clustered in similar group. Use the SOM for clustering data without knowing the class memberships of the input data. The SOM can be used to detect features inherent to the problem and thus has also been called SOFM, the Self-Organizing Feature Map. Use the SOM for clustering data without knowing the class memberships of the input data. The SOM can be used to detect features inherent to the problem and thus has also been called SOFM, the Self-Organizing Feature Map.

Provides a topology preserving mapping from the high dimensional space to map units. Map units, or neurons, usually form a two-dimensional lattice and thus the mapping is a mapping from high dimensional space onto a plane. The property of topology preserving means that the mapping preserves the relative distance between the points. Points that are near each other in the input space are mapped to nearby map units in the SOM. The SOM can thus serve as a cluster analyzing tool of high-dimensional data.

Here the system is totally based on the Matlab techniques and this is not the development of a new system it is only the research of the listing of self organizing map. Here we are using only the tools and techniques of the software engineering. No any software development model is using over the project. An artificial neural network (ANN), also called a simulated neural network (SNN) or commonly just neural network (NN) is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network..They can be used to model complex relationships between inputs and outputs.

[...] PROBLEM DEFINITION SOM TRAINING: A self-organizing map consists of two layers of neurons: an input layer and a so-called competition layer. The weights of the connections from the input neurons to a single neuron in the competition layer are interpreted as a reference vector in the input space. That is, a self-organizing map basically represents a set of vectors in the input space: one vector for each neuron in the competition layer. A self-organizing map is trained with a method that is called competition learning: When an input pattern is presented to the network, that neuron in the competition layer is determined, the reference vector of which is closest to the input pattern. [...]


[...] a new system it is only the research of the listing of self organizing map. Here we are using only the tools and techniques of the software engineering. No any software development model is using over the project. Neural Network An artificial neural network also called a simulated neural network (SNN) or commonly just neural network is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. [...]


[...] MATLAB provides the following types of functions for performing mathematical operations and analyzing data: Matrix manipulation and linear algebra Polynomials and interpolation Fourier analysis and filtering Data analysis and statistics Optimization and numerical integration Ordinary differential equations (ODEs) Partial differential equations (PDEs) Sparse matrix operations MATLAB can perform arithmetic on a wide range of data types, including doubles, singles, and integers. Add-on toolboxes (available separately) provide specialized mathematical computing functions for areas including signal processing, optimization, statistics, symbolic math, partial differential equation solving, and curve fitting. [...]

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