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NeuroXL Clusterizer 4.0.5 by OLSOFTDownload

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Users' rating   (1 votes) Rate it!
License Shareware / $99.95
Downloads 140
Publisher
File size 2.3 MB
Date added 6-Feb-2015
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Editor's review:

This is a software tool that helps clusterize data in Excel sheet with the help of a neural network. Results are presented as graphs and charts to help you to interpret real world data and recognition of patterns.

NeuroXL Clusterizer is a neural network add-in for Microsoft Excel. It is designed to aid users in real-world data mining and pattern recognition. The way these recognition or data clustering is done is not visible to the user and thus he is not stressed with the complexity, but is presented with the data and any patterns found. NeuroXL Clusterizer uses already proven and accepted algorithms and techniques so that there are no doubts about the veracity of the results. Neural networks, as we know is a network of interconnected processors that work somewhat like the human brain. Connections are formed through a training period through which a particular approach to solving a problem is emphasized.

This NeuroXL Clusterizer software uses a self-organizing neural network to categorize by learning trends and relationships within a set of data. You could use one of five transmission functions available, threshold, Hyperbolic tangent, Zero-based log-sigmoid, Log-sigmoid and Bipolar sigmoid. There are a range of industrial problems that cannot be fitted into any system of analytical solutions, this then provides a tool for such problem solving through the power of neural network that has been suitably trained. For example, in finance, science, and business, analysts are often faced with the task of cluster analysis based on historical or measured data. Stock market analysts want to categorize a group of stocks as buy, sell, or hold and such other situations. A major difficulty faced by such analysts is that the data to be categorized can often be quite complex, with numerous interrelated variables. The time and effort required to develop a model to solve accurately such clusterization problems can be significant. You could try this tool out in your situation and see if it fits.


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