Different Levels of Analysis are available:
· Artificial Neural Networks: Non-linear predictive models that are taught through training and resemble biological neural networks in the structure.
· Genetic Algorithms: Optimization techniques using methods such as genetic combination, mutation and natural selection in a design based on the concepts of natural development.
· Decision Trees: Trees shaped structures representing sets of decisions. These decisions will produce rules for the classification of the data.
· Nearest Neighbor Method: A technique that classifies each record in a data set is based on a combination of categories of k(s) more similar to a historical dataset sometimes called k- nearest neighbor technique.
· Rule induction: Mining useful if-then rules from data based on statistical significance.
· Imaging Data: The visual interpretation of complex relationships in multi-dimensional data. Graphics tools are used to visualize data relationships.
· Rule induction: Mining useful if-then rules from data based on statistical significance.
· Imaging Data: The visual interpretation of complex relationships in multi-dimensional data. Graphics tools are used to visualize data relationships.