Comparative Study between Traditional and Heuristic Approach on Close-degree Concept Lattice for better Attribute Reduction

Concept lattice is a new mathematical tool for data analysis and knowledge processing. There is an increasing interest on application of concept lattices in the different information systems. The concept lattice may be used for representation of the concept generalization structure generated from the underlying data set. 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 simpler. The paper presents a modified lattice building algorithm where the generated concept nodes may contain not only the attributes of the children nodes but some other generalized attributes, too. The generalization structure of the attributes is called attribute lattice. In this approach we find concept lattice, Attribute Concept lattice and, Formal Concept Lattice based on all the deduction we find the Heuristic Result which provide better attribute reduction. We also discuss a comparative study between the traditional and the new heuristic approach.