International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 3, No. 3, 8 Jun. 2011
Cover page and Table of Contents: PDF (size: 226KB)
Application of Cloud Theory in Association Rules
Full Text (PDF, 226KB), PP.36-42
Views: 0 Downloads: 0
Author(s)
Zhaohong Wang
1,*
Index Terms
Cloud Theory, Association Rules, Trapezium-Cloud Model, Conception Division, Frequent Item Sets
Abstract
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.
Cite This Paper
Zhaohong Wang, " Application of Cloud Theory in Association Rules", International Journal of Information Technology and Computer Science(IJITCS), vol.3, no.3, pp.36-42, 2011. DOI:10.5815/ijitcs.2011.03.06
Reference
[1]J. Han, and M. Kamber, Data Mining Concepts and Techniques, 2nd ed., Morgan Kaufmann Publishers: 2006, pp.98-112．
[2]Mehmed Kantardzic, Datamining Concepts, Models, Methods and Algorithms, Beijing: Tsinghua University Press, 2009．
[3]Lu Qiu-gen, Fuzzy clustering algorithm and implementation, Computer Knowledge and Technology, 2008, vol.3 (27) , pp.1987-1990．
[4]Liao Zhian, Data Fuzzy processing, Journal of Ezhou University, 2005, vol. 12(6), pp.49-51.
[5]Li De-yi and Liu Chang-yu, Discussion of the universal nature on the normal cloud model. China Engineering Science, 2004, vol. 6, pp. 28-33.
[6]Liu Chang-Yu, Li Deyi, Du Yi, Statistical analysis of normal cloud model， Information and Control, in Chinese, 2005, 34 (2), pp. 236-239
[7]Li Deyi, Meng Haijun, Shi Xuemei, Membership cloud and membership cloud generator, Computer Research and Development, in Chinese, 1995, 32 (6) pp. 15-20
[8]Li Deyi. Triple inverted pendulum control and dynamic balance based on clouds model, China Engineering Science, in Chinese, 1999, l (2), pp. 41-46
[9]Li Deyi, Du Yi, Uncertainty Artificial Intelligence， Beijing: National Defense Industry Press, 2005
[10]Lu Huijun, Wang Ye, Li Deyi, Reverse cloud application in the qualitative evaluation, Journal of Computers, 2003, 26 (8) pp. 1009-1014
[11]Fan Zhoutian, Fuzzy Matrix Theory and Applications, Beijing: Science Press, 2006.
[12]LuJianjiang, Zhang Yanlin, Song Zilin, Fuzzy Association Rules and Application, Beijing: Science Press, 2008.
[13]Wang Hu, Mao Wenting, telecom customer behavior study based on cloud model and association rules, Journal of Wuhan University of Technology (Information and Management Engineering), 2009, vol.31 (5), pp.77-79．
[14]Yan Yuejin, Li Zhoujun, Chen Huowang, Efficient maximal frequent itemsets Mining Based on FP-Tree, Journal of Software, 2005, vol.16(2), pp.215-222.
[15]J Han, J Pei, Y Yin, Mining Frequent Patterns without Candidate Generation A Frequent Pattern Tree Approach, data Mining and knowledge discovery, 2004, vol. 8, pp.53-87.