An Agent Based Analysis of Knowledge Discovery in Databases

Discovering useful knowledge from database is referred as KDD. In the quest of knowledge possible interpretation of patterns and evaluation makes the decision of what is knowledge and what is not. It also includes the choice of programming schemes, systematic series of actions, sampling, and the state or fact of the data prior to the data mining step. Data mining refers to the application of algorithms for extracting patterns from data without the additional steps of the KDD process. The Knowledge Discovery in Databases model is a step by step process for finding interesting patterns in large amounts of data. Data mining is one step in the process. This paper “An Agent Based Analysis of Knowledge Discovery in Databases “defines the KDD process and discusses the agent work for the KDD process as a potential for performance evaluation . The focus of this paper is to first summarize exactly what the KDD process is and the work done by the KDD process, and association of the KDD agent and the software agent and the agent work with KDD. The activities of these agents are coordinated when a process is instantiated and executed. The vehicle for agent coordination during process execution is an agenda management system (AMS) is also seen.