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Unit 3: Data Mining Techniques

of prediction, classification or control, neural networks are being introduced. This sweeping notes
success can be attributed to a few key factors:
1. Power: Neural networks are very sophisticated modeling techniques capable of modeling
extremely complex functions. In particular, neural networks are nonlinear (a term which
is discussed in more detail later in this section). For many years linear modeling has been
the commonly used technique in most modeling domains since linear models have well-
known optimization strategies. Where the linear approximation was not valid (which was
frequently the case) the models suffered accordingly. Neural networks also keep in check
the curse of dimensionality problem that bedevils attempts to model nonlinear functions
with large numbers of variables.
2. Ease of use: Neural networks learn by example. The neural network user gathers
representative data, and then invokes training algorithms to automatically learn the
structure of the data. Although the user does need to have some heuristic knowledge of
how to select and prepare data, how to select an appropriate neural network, and how
to interpret the results, the level of user knowledge needed to successfully apply neural
networks is much lower than would be the case using (for example) some more traditional
nonlinear statistical methods.
Neural networks are also intuitively appealing, based as they are on a crude low-level model of
biological neural systems. In the future, the development of this neurobiological modeling may
lead to genuinely intelligent computers.

applications for neural networks

Neural networks are applicable in virtually every situation in which a relationship between the
predictor variables (independents, inputs) and predicted variables (dependents, outputs) exists,
even when that relationship is very complex and not easy to articulate in the usual terms of
“correlations” or “differences between groups.” A few representative examples of problems to
which neural network analysis has been applied successfully are:
1. Detection of medical phenomena: A variety of health-related indices (e.g., a combination
of heart rate, levels of various substances in the blood, respiration rate) can be monitored.
The onset of a particular medical condition could be associated with a very complex (e.g.,
nonlinear and interactive) combination of changes on a subset of the variables being
monitored. Neural networks have been used to recognize this predictive pattern so that
the appropriate treatment can be prescribed.

2. Stock market prediction: Fluctuations of stock prices and stock indices are another
example of a complex, multidimensional, but in some circumstances at least partially-
deterministic phenomenon. Neural networks are being used by many technical analysts
to make predictions about stock prices based upon a large number of factors such as past
performance of other stocks and various economic indicators.

3. Credit assignment: A variety of pieces of information are usually known about an applicant
for a loan. For instance, the applicant’s age, education, occupation, and many other facts
may be available. After training a neural network on historical data, neural network
analysis can identify the most relevant characteristics and use those to classify applicants
as good or bad credit risks.
4. Monitoring the condition of machinery: Neural networks can be instrumental in cutting costs
by bringing additional expertise to scheduling the preventive maintenance of machines.
A neural network can be trained to distinguish between the sounds a machine makes when
it is running normally (“false alarms”) versus when it is on the verge of a problem. After
this training period, the expertise of the network can be used to warn a technician of an
upcoming breakdown, before it occurs and causes costly unforeseen “downtime.”

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