(yX) for Excel 1.1

(yX) for Excel 1.1 offers you a powerful knowledge mining software that works with data stored in Microsoft Excel for building predictive and descriptive models from this data autonomously and easily. It supports both major releases of Microsoft Excel, 2004 and 2008. The modeling engine of KnowledgeMiner (yX) for Excel implements unique modeling technologies which are built on the principles of self-organization: Learning from noisy data an unknown relationship between output and input of any given system in an evolutionary way from a very simple model to an optimal complex one which generalizes well.

KnowledgeMiner (yX) for Excel implements a completely redesigned and redeveloped modeling engine called (yX). It is based on the modeling technologies also found in our established KnowledgeMiner 6.0 software, which has been successfully used by our customers for more than 10 years.

It is 64-bit parallel software. 64-bit and parallelization allows (yX) to take full advantage of multi-processor and/or multi-core based Macs. It scales to the number of processing elements found at runtime (fig. 1).

Major Features:

1. Only minimal, uncertain a priori information about the system is required. That means, even if you have no experience in modeling, data analysis or designing a neural network you will be able to model, analyze and predict complex relationships of nearly any kind of system.
   
2. A very fast and effective learning process for a personal computer. That means you can solve problems on your desktop in a reasonable time which you may have never thought possible.
   
3. Modeling short and noisy data samples. That means, you can deal with a problem as is and don't have to construct artificial conditions for your modeling method to get it work.
   
4. Output of an optimally complex model. Generally you can be sure to get a model at the end of the automated modeling process which can be expected not to be overfitted. Overfitted models are not able to predict inherent relationships between variables.
   
5. Output of an analytical model as a transparent explanation component. That means, you can evaluate the analytical model to explain the obtained results immediately after modeling.

Enhancements:

• Automatic generation of graphs for regression models in Excel for both modeling and model export
• Significant performance improvement for building regression models on data sets with large number of samples
• Essential memory requirement reduction for regression modeling
• Tutorial and examples

• Mac OS X 10.5 (Leopard) or newer
• Any multi-core Intel-based Mac to run software concurrently (recommended), PPC Macs to run software sequentially, only
• Microsoft Excel 2004 or 2008