### Kevin Voges (Griffith University, Brisbane, Australia)

#### Neural networks and evolutionary algorithms in nonlinear data analysis and data mining

*Wednesday 11 February 1998 at 15.30, JCMB 6324*

##### Abstract

Over the years I have become increasingly concerned about the
simplifying assumptions required in the standard application of the
linear variate to data analysis. It seems to me that the simplifying
assumptions (non-linearity, etc) do not hold up in the real world.
I believe that one possible solution to the limitations imposed by
these assumptions is the use of adaptive systems techniques,
including such approaches as neural networks and evolutionary
algorithms. My eventual aim is to apply these techniques to large
scale marketing databases, an area which is starting to be known as
knowledge discovery or data mining. To achieve that aim, I have been
working on demonstrations of the technique on small scale datasets.
The current dataset I am using is a questionnaire based survey of
training needs in a professional organisation. Traditional
approaches might use discriminant analysis or regression. What I am
attempting to do is to develop an alternative approach to
interpreting this dataset using an evolutionary algorithm. Such an
approach requires the development of different ways of representing
the data in a model, and different measures of the fitness of this
representation. More generally I am interested in the impact and
application of computational intelligence to marketing, and in the
marketing of high-technology products.

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