Abstract
In this chapter, an agent-based fuzzy data mining structure was developed to process and evaluate data with an enlargement in the knowledge dimension, and to build a rule structure for the system. Within the developed system, the focus was on the operation feature of the fuzzy data mining structure, which is the same for each agent composing the system. The suggested association rules are derived from a relational database. Future tasks of the system will be estimated when the system performs fuzzy data mining more quickly thanks to the distributed, autonomous, intelligent, and communicative agent structure of the suggested agent-based fuzzy rule mining system. In fuzzy rule mining, the system will primarily examine and group the relational database in databases of the agents with fuzzy logic and then will shape the rule base of the system by applying the fuzzy data mining method to these data.
Key Terms in this Chapter
Fuzzy Partition Approach: It represents the fuzzy cluster structure.
Agent: It is a system that fulfills the independent functions, perceives the outer world, and establishes the linking among the agents through its software.
Bid: A bid is the unit of communication among the agents during messaging.
Production Process Control: It includes the selecting of the proper production process in determined optimization parameters for improving process quality and reducing processing costs.
Agent Mining Structure: An agent mining structure contains the distributed mining activities that consist of each agent mechanism in the whole system.
Fuzzy Agent: It is a software agent that runs on fuzzy logic in a system of data processing and decision making.
MultiAgent System: It is a flexible, incorporated network of software agents that interact to solve the problems that are beyond the individual capacities or knowledge of each problem solver.
System: It is a set of elements considered to act as a single goal-oriented entity.
Intelligent Agent: It consists of a sophisticated intelligent computer program that is situated, independent, reactive, proactive, flexible, and that recovers from failure and interacts with other agents.
Fuzzy Grid: It represents the degree of importance according to classification rules that are generated by partitioning each data attribute with various linguistic values.
Fuzzy SQL Query: Fuzzy SQL allows the system to make flexible queries about crisp or fuzzy attributes in fuzzy relational databases. Actually, there are two main proposals: SQLf for fuzzy queries to classical databases, and FSQL for fuzzy queries to classical or fuzzy databases.