Recursive data mining strategy using close-degree of concept lattice for knowledge discovery process with granularity

Abstract

Concept lattice is a new mathematical tool for data analysis and knowledge processing. Attribute reduction is very important in the theory of concept lattice because it can make the discovery of implicit knowledge in data easier and the representation is simple. Knowledge discovery has received more and more attention from the business community for the last few years. One of the most important and challenging problems in it is the definition of discovery process model, which are well understood, efficiency, and quality of outcome. Infrastructure investment decisions consider future infrastructure demand projections from freight models, the quality of which depends on fidelity of input recursive data. Recursive distributions at both the levels: federal and county are insufficient for incorporating the effect of freight-related traffic on metropolitan-level transportation infrastructure. This paper describes a detail discussion about clusters based concept lattice with a recursive approach. First, we present a close degree of concept to measure the close-degree of two concepts with the attributes recursion. A higher order mining method embedded in the process achieved after monitoring several aspects and identifying changes statically and tracing trends dynamically this method works on several data mining analysis.