Page 81 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Unit 4: Data Mining Classification

notes
figure 4.6: transformation of a Decision tree into Decision rules

A rule can be “pruned” by removing any condition in its antecedent that does not improve the
estimated accuracy of the rule. For each class, rules within a class may then be ranked according
to their estimated accuracy. Since it is possible that a given test sample will not satisfy any rule
antecedent, a default rule assigning the majority class is typically added to the resulting rule
set.

4.8 neural network based algorithms

Artificial Neural Network

An artificial neural network is a system based on the operation of biological neural networks,
in other words, is an emulation of biological neural system. Why would be necessary the
implementation of artificial neural networks? Although computing these days is truly advanced,
there are certain tasks that a program made for a common microprocessor is unable to perform;
even so a software implementation of a neural network can be made with their advantages and
disadvantages.

Advantages of Neural Network

The advantages of neural network are:
1. A neural network can perform tasks that a linear program can not.

2. When an element of the neural network fails, it can continue without any problem by their
parallel nature.
3. A neural network learns and does not need to be reprogrammed.
4. It can be implemented in any application.

5. It can be implemented without any problem.

Disadvantages of Neural Network

The disadvantages of neural network are:
1. The neural network needs training to operate.
2. The architecture of a neural network is different from the architecture of microprocessors
therefore needs to be emulated.
3. Requires high processing time for large neural networks.

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