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NeuroXL Clusterizer is a neural net data clustering add-in for Microsoft Excel
| Publisher: |
OLSOFT |
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| Downloads: |
1215 |
| Software Type: |
Shareware, 99.95 |
| File Size: |
2.26M |
| OS: |
Windows All |
| Update Date: |
28 November, 2019 |
NeuroXL Clusterizer is a neural network add-in for Microsoft Excel.
NeuroXL Clusterizer is an add-in for Excel designed to aid experts in real-world data mining and pattern recognition tasks. It hides the underlying complexity of neural network processes while providing graphs and statistics for the user to easily understand results. NeuroXL Clusterizer uses only proven algorithms and techniques, and integrates seamlessly with Microsoft Excel.
Neural networks are a proven, widely used technology to solve complex clustering problems. Loosely modeled after the human brain, neural networks are interconnected networks of independent processors that, by changing their connections (known as training), learn the solution to a problem. NeuroXL Clusterizer software implements self-organizing neural networks, which perform categorization by learning the trends and relationships within your data.
NeuroXL Clusterizer is a powerful, easy-to-use and affordable solution for advanced cluster analysis of simple and complex data. By harnessing the latest advances in artificial intelligence and neural network technology, it delivers accurate and fast classifications. Designed as an add-on to Microsoft Excel, it is easy to learn and use and requires no importing or exporting of data. Five transmission functions are available to choose: Threshold, Hyperbolic tangent, Zero-based log-sigmoid, Log-sigmoid and Bipolar sigmoid. Moreover, it is possible to save the trained network and then load it when necessary.
NeuroXL Clusterizer can be applied to solve problems in numerous industries and disciplines, including finance, business, medicine, and research science.
NeuroXL Clusterizer's ability to handle numerous, often-interrelated variables makes it widely applicable for market data cluster analysis. For example, a trader may wish to cluster stocks as buy, hold, or sell being based on historical data.
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