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# Data Applied:Similarity

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Data Applied:Similarity

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### Data Applied:Similarity

1. 1. Data-Applied.com: Similarity<br />
2. 2. Introduction<br />Visualization of data having high dimensions is very difficult<br />The kohonen network enables us to represent high dimensional data in lower dimensions while keeping the topological information in it like similarity etc. intact<br />Kohonen networks or self organizing maps learn to classify data without supervision unlike artificial neural networks<br />A SOM does not need a target output to be specified unlike many other types of network. The learning steps are:<br />
3. 3. Components of Kohonen network<br />Kohonen networks consist of:<br />Input nodes which are equal in number to the dimension of the data<br />A grid of nodes which are used for classification, each having a weight vector equal to the dimension of input and connected to all inputs<br />
4. 4. Learn Algorithm Overview<br />Each node's weights are initialized.<br />A vector is chosen at random from the set of training data and presented to the lattice.<br />Every node is examined to calculate which one's weights are most like the input vector. The winning node is commonly known as the Best Matching Unit (BMU).<br />The radius of the neighbourhood of the BMU is now calculated. This is a value that starts large, typically set to the 'radius' of the lattice,  but diminishes each time-step. Any nodes found within this<br />
5. 5. Learn Algorithm Overview<br />Each node's weights are initialized.<br />A vector is chosen at random from the set of training data and presented to the lattice.<br />Every node is examined to calculate which one's weights are most like the input vector. The winning node is commonly known as the Best Matching Unit (BMU).<br />The radius of the neighbourhood of the BMU is now calculated. This is a value that starts large, typically set to the 'radius' of the lattice,  but diminishes each time-step. Any node found within<br />
6. 6. Learn Algorithm Overview(contd..)<br /> radius are deemed to be inside the BMU's neighbourhood.<br />5. Each neighbouring node's (the nodes found in step 4) weights are adjusted to make them more like the input vector. The closer a node is to the BMU, the more its weights get altered.<br />6. Repeat step 2 for N iterations.<br />
7. 7. Similarity using Data Applied’s web interface<br />
8. 8. Step1: Selection of data<br />
9. 9. Step2: Selecting Similarity<br />
10. 10. Step3: Result<br />
11. 11. Visit more self help tutorials<br /><ul><li>Pick a tutorial of your choice and browse through it at your own pace.
12. 12. The tutorials section is free, self-guiding and will not involve any additional support.
13. 13. Visit us at www.dataminingtools.net</li>