Rule induction using ant colony optimization for mixed variable attributes

Data mining is defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data."

One technique used in data mining is rule induction where the desired output is a set of Rules or Statements that characterize the data. Within the rule induction paradigm, Swarm intelligence (SI) is a technique whereby rules may be discovered through the study of collective behavior in decentralized, self-organized systems, such as ants. Ant-Miner is a rule induction algorithm that uses SI techniques to form rules. Ant-Miner uses a discretization process to deal with continuous attributes in the data. Discretization transforms numeric attributes into nominal attributes. Discretization may suffer from a loss of information, since the real relationship underlying individual values of a numeric attribute is unknown.

The objective of this thesis is to improve Ant-Miner so that it can process with continuous attributes directly using multi modal functions.