Association Classification Algorithm Based on Concept Correlation

摘要

This paper presents an algorithm of association classification based on the correlation information between antecedent and consequent. The new method assumes that data properties are basic vectors of m dimensions, and each of the data is viewed as a sum vector of all the property-vectors. It suggests a novel distance algorithm to get the distance of every pair of the property based on similar information of the basic property vectors. An algorithm of association classification is also presented based on correlation computing formula composed of property vectors and projections of each other. Detailed simulation analysis demonstrates that new algorithm is of high efficiency of space and time and is more stable.