This paper introduces the problem of data mining association rules. We adopt the iterative method to enlarge the size of the item set gradually and describe the hierarchical algorithm in detail. The hierarchical algorithm produces a larger provisional sets based on the obtained frequent item sets and make sure that those provisional sets which will never be frequent item set are ignored under the premise of the known information. Finally, an improving algorithm which is to combine the last several procedures of iteration into a single scan of the database D. Mainly because that the more backwards the iterative processes approach the end, the less the provisional sets are there.
KEYWORDS: Neural networks, Data modeling, Differential equations, Systems modeling, Analytical research, Process modeling, Data processing, Control systems, Lutetium, Artificial intelligence
The modality and features of gray differential equations and the features of differential parameters have been researched. The gray attribute of the differential equation parameters also has been analyzed. Based on studying one dimensional gray problem modeling and neural network modeling, a method of whitening the parameters of gray differential equation using gray neural network- GNNM was put forward. Furthermore, we also studied two-dimensional gray problem and built GNNM.
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