American Heuristics Corporation - THOT® AFFM Main Page

Advanced software Technologies for real-world solutions!

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Introducing THOT^® AFFM:

As means of data collection have become more capable, the need for non-linear, multi-variate modeling techniques has become more and more apparent. Data collection streams and the number of meaningful variables are broadening. Traditional data modeling methods simply were not designed to work with one hundred or more variables.

In answer to this, the last decade has seen the emergence of machine learning or artificial intelligence as a means of modeling complex patterns in data. Technologies such as neural networks, genetic algorithms and fuzzy logic have been very effective at finding interrelationships between multiple variables and modeling real-world, non-linear data. However, the business world has been reluctant to accept these methods due to computational intensity, and most of all, the inability to clearly trace and explain results.

With AFFM, data mining may be performed by savvy business users rather than an analytical team. AFFM may also be integrated in such a way that results are translated into domain specific language through a rule-driven knowledge-base. As well, comparing weights between any two or more variables is straight forward for convenient data visualization. This is particularly useful since the weights contain both vector correlation and pattern matching experience. AHC sees AFFM as the next significant step in adaptive pattern based modeling.