Page 56 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Data Warehousing and Data Mining

notes
figure 3.2: Decision tree structure

3.5 neural networks

Neural Networks are analytic techniques modeled after the (hypothesized) processes of learning
in the cognitive system and the neurological functions of the brain and capable of predicting new
observations (on specific variables) from other observations (on the same or other variables) after
executing a process of so-called learning from existing data. Neural Networks is one of the Data
Mining techniques.
figure 3.3: neural network

The first step is to design a specific network architecture (that includes a specific number of
“layers” each consisting of a certain number of “neurons”). The size and structure of the network
needs to match the nature (e.g., the formal complexity) of the investigated phenomenon. Because
the latter is obviously not known very well at this early stage, this task is not easy and often
involves multiple “trials and errors.”
The new network is then subjected to the process of “training.” In that phase, neurons apply
an iterative process to the number of inputs (variables) to adjust the weights of the network in
order to optimally predict (in traditional terms one could say, find a “fit” to) the sample data on
which the “training” is performed. After the phase of learning from an existing data set, the new
network is ready and it can then be used to generate predictions.
Neural networks have seen an explosion of interest over the last few years, and are being
successfully applied across an extraordinary range of problem domains, in areas as diverse as
finance, medicine, engineering, geology and physics. Indeed, anywhere that there are problems

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