Most neural networks are software programs that measure the difference between a guess and the target pattern, make an adjustment, guess again, and iterate the process, until the guess is within a given tolerance of the target. A traditional neural network consists of multiple layers, typically three, with an input layer, a middle processing layer, and an output layer. Each layer contains several nodes that are connected to the nodes in the successive layer. An advantage of neural networks is that they provide a powerful way to map a collection of data. A disadvantage is that this power can go awry if the training data is not carefully prepared to address the problem of interest to the researcher.
Decision trees represent a set of rules.
The data mining application analyzes the data, then builds a tree of these rules to represent the trends. The trends can be used to identify different classes or continuous variables. Decision trees can also be called expert systems. A genetic algorithm is a learning method used to guide the data mining application toward a consistent solution or pattern that describes the data. A genetic algorithm consists of iteratively running multiple simulations through a model.