Quantitative Association Rules Mining Method Based on Trapezium Cloud Model
Zhaohong Wang · 2010
The quantitative association rules mining method is difficult for their values are too large. The usual means is dividing quantitative Data to discrete conception. The trapezium Cloud model combines ambiguity and randomness organically to fit the real world objectively, divide quantitative Data with trapezium Cloud model to create concepts, the concept cluster within one class, and separated with each other. So the quantitative Data can be transforms to Boolean data well, the Boolean data can be mined by the mature Boolean association rules mining method.