| Program Detail |
: There is an abundance of data that is rapidly being generated. Intelligent software tools are increasingly needed to process and filter the data, detect new patterns and similarities. Large databases of information create great opportunities for the application of data mining methods. Conventional computer science algorithms even though useful are not powerful enough in solving many of the knowledge discovery and other problems.
Data mining approaches (e.g., neural networks, decision trees, regression trees, clustering) are ideally suited for domains characterized by the presence of large amounts of noisy data, and the absence of general theories or hypothesis about the data. The fundamental idea behind these approaches is to learn automatically from the data, creating a theory, hypothesis or a model, through a process of inference, model fitting, or learning from examples. |
