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. [...]
[...] As a result, one line of MATLAB code can often replace several lines of At the same time, MATLAB provides all the features of a traditional programming language, including arithmetic operators, flow control, data structures, data types, object-oriented programming and debugging features. A communications modulation algorithm that generates 1,024 random bits, performs modulation, adds complex Gaussian noise, and plots the result--all in just 9 lines of MATLAB code. Click image to see enlarged view. MATLAB lets you execute commands or groups of commands one at a time, without compiling and linking, enabling you to quickly iterate to the optimal solution. [...]
[...] The Self-Organizing Map (SOM) is a powerful neural network method for the analysis and visualization of high-dimensional data. It maps nonlinear statistical relationships between high-dimensional measurement data into simple geometric relationships, usually on a two-dimensional grid. This process, of reducing the dimensionality of vectors, is essentially a data compression technique known as vector quantization. In addition, the Kohonen technique creates a network that stores information in such a way that any topological relationships within the training set are maintained. The mapping roughly preserves the most important topological and metric relationships of the original data elements and, thus, inherently clusters the data. [...]
[...] Clustering is the classification of similar objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait - often proximity according to some defined distance measure. Machine learning typically regards data clustering as a form of unsupervised learning. Besides the term data clustering (or just clustering), there are a number of terms with similar meanings, including cluster analysis, automatic classification, numerical taxonomy, botryology and typological analysis. [...]
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