Application of Cloud Theory in Association Rules
Автор: Zhaohong Wang
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 3 Vol. 3, 2011 года.
Бесплатный доступ
The data mining is to discover knowledge from the database, quantitative association rules mining method is difficult for their values are too large. The usual means is dividing quantitative Data to discrete conception. The Cloud model combines fuzziness and randomness organically, so it fits the real world objectively, a new method to mine association rules from quantitative data based on the cloud model was proposed, which first take the original data distribution in the database into account, and then use the trapezoidal cloud model to complicate concepts division, and transforms qualitative data to the quantitative concept, in the conversion take account of the basic characteristics of human behavior fully, divides quantitative Data with trapezium Cloud model to create discreet 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 to find useful knowledge.
Cloud Theory, Association Rules, Trapezium-Cloud Model, Conception Division, Frequent Item Sets
Короткий адрес: https://sciup.org/15011624
IDR: 15011624
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