SEARCH WITHIN CONTENT
Citation Information : International Journal of Advanced Network, Monitoring and Controls. Volume 2, Issue 3, Pages 20-26, DOI: https://doi.org/10.1109/iccnea.2017.87
License : (CC BY-NC-ND 4.0)
Published Online: 10-April-2018
To come over the limitations of Apriori algorithm and association rule mining algorithm based on Genetic Algorithm (GA), this paper proposed a new association rule mining algorithm based on the population-based incremental algorithm (PBIL), which is a kind of distribution estimation algorithms. The proposed association rule-mining algorithm keeps the advantages of GA mining association rules in coding and the fitness function. Through using probability vector possessing learning properties to update the population, the algorithm increases the convergence speed and enhances the searching ability, compared to GA. In the experiment of mining association, rules in blood indices data, PBIL algorithm performs better not only in running time, convergence speed, but also achieve better searching results. Meanwhile, this paper proposed a parallel algorithm for association rule mining based on PBIL and designed a system architecture based on cloud computing for blood indices analysis, providing a good example to apply the new algorithm to cloud computing.
Jiawei Han. Data Mining: concept and technology [M]. Beijing: China Machine Press, 2004:137-147.J.
Xiaomin Di. Research on Mining Common Risk Factors of Multi-diseases and Predicting Disease [D]. Taiyuan University of Technology, 2013.
Yin Li, Changxiu Cao, Jianghong Ren, etc. Application of General Algorithm (GGA) in the Improvement of Apriori Algorithm [J]. Computer and Modernization, 2004(11):1-3.
Shiwei Chen. Research on Association Rule Mining Based on Interest and Genetic Algorithm [D]. Zhejiang University, 2012.
Donghao Chen, Hongwei Li, Tieying Zhang, etc. Application of Improved PSO Algorithm in Spatial Association Rule Mining [J]. Science of Surveying and Mapping, 2016, 41(2):168-172.
Lämmel R. Google’s MapReduce programming model — Revisited [J]. Science of Computer Programming, 2008, 70(1):1-30.
Sheng Zhang. An Apriori—based Algorithm of Association Rules based on Cloud Computing [J]. Communications Technology, 2011, 44(6):141-143.
Zhengchan Rao, Nianbo Fan. A review of associative rule mining Apriori algorithm[J]. Computer Era, 2012(9):11-13.
Guoyan Xu, Yuqing Shi. Application of Genetic Algorithm in Association Rule Mining[j] Computer engineer, 2002, 28(7):122-124.
Zhang Q. On Stability of Fixed Points of Limit Models of Univariate Marginal Distribution Algorithm and Factorized Distribution Algorithm [J]. IEEE Transactions on Evolutionary Computation,2004,8(1):80-93.
Shude Zhou, Zenqi Sun. A Survey on Estimation of Distribution Algorithm [J]. Acta Automatica Sinica, 2007, 33(2):113-124.
H. Muhlenbein, T. Mahnig. Convergence theory and application of the factorized distribution algorithm [J]. Comput. Inf. Technol. 1999,7(1):19–32.
Qin Y J, Sun J S, Wang B Y. The differences of the blood routine indices in patients with fatty liver and non-fatty liver[J]. Journal of ClinicalHepatology,2010.
Qiang Xu, Zhenjiang Wang. Practice of Cloud-computing Application Developing. Beijing: China Machine Press, 2012:64-67.