The Research of Distributed Data Mining Knowledge Discovery Based on Extension Sets

Distributed Data Mining(DDM) has evolved into an important and active area of research because of theoretical challenges and practical applications associated with the problem of extracting, interesting and previously unknown knowledge from very large real-world databases. Extension Set Theory is a mathematical formalism for representing uncertainty that can be considered an extension of the classical set theory. It has been used in many different research areas, including those related to inductive machine learning and reduction of knowledge in Distributed data-based systems. Extenics is a theory to solve the contradiction problem, it will be a new way to look for and find knowledge through analysis the contradiction and transformation the result of the data mining using the extension methods. In this paper, introduced the matter-element and extension set that is the base of the extenics, researched the way to find out and generate the new knowledge that help by the divergence, change and transformation based on the extension The main aim is to show how Extension sets can be effectively used to extract knowledge from large databases.