This approach groups data according to similarities or classes. There are many types of classification techniques and numerous automated tools available. Although the Bayesian approach uses probabilities and a graphical means of representation, it is also considered a type of classification. Bayesian networks are typically used when the uncertainty associated with an outcome can be expressed in terms of a probability. This approach relies on encoded domain knowledge and has been used for diagnostic systems.
Automated tools are available both commercially and in the public domain.
If redundant and uninteresting data is eliminated, the task of discovering patterns in the data is simplified. The pattern discovery and data cleaning techniques are useful for reducing enormous volumes of application data, such as those encountered when analyzing automated sensor recordings. Once the sensor readings are reduced to a manageable size using a data cleaning technique, the patterns in the data may be more easily recognized. Automated tools using these techniques are available both commercially and in the public domain. The Decision Tree Approach uses production rules, builds a directed acyclical graph based on data premises, and classifies data according to its attributes.