基于预判筛选的高效关联规则挖掘算法

赵学健; 孙知信; 袁源

doi:10.11999/JEIT151107

基于预判筛选的高效关联规则挖掘算法

doi: 10.11999/JEIT151107

基金项目:

国家自然科学基金(61373135, 61401225, 61502252, 61201160)，江苏省基础研究计划(自然科学基金)(BK20140883, BK20140894, BK20131377)，中国博士后科学基金(2015M581844)，江苏省博士后科研资助计划项目(1501125B)，南京邮电大学校级科研基金(NY214101, NY215147)

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- 被引次数: 0
出版历程
- 收稿日期: 2015-09-29
- 修回日期: 2016-02-26
- 刊出日期: 2016-07-19

An Efficient Association Rule Mining Algorithm Based on Prejudging and Screening

Funds:

The National Natural Science Foundation of China (61373135, 61401225, 61502252, 61201160), Natural Science Foundation of Jiangsu Province of China (BK20140883, BK20140894, BK20131377), China Postdoctoral Science Foundation Funded Project (2015M581844), Jiangsu Planned Projects for Postdoctoral Research Funds (1501125B), NUPTSF (NY214101, NY215147)

摘要

摘要: 关联规则分析作为数据挖掘的主要手段之一，在发现海量事务数据中隐含的有价值信息方面具有重要的作用。该文针对Apriori 算法的固有缺陷，提出了AWP (Apriori With Prejudging) 算法。该算法在Apriori 算法连接、剪枝的基础上，添加了预判筛选的步骤，使用先验概率对候选频繁k项集集合进行缩减优化，并且引入阻尼因子和补偿因子对预判筛选产生的误差进行修正，简化了挖掘频繁项集的操作过程。实验证明AWP算法能够有效减少扫描数据库的次数，降低算法的运行时间。
- 数据挖掘 /
- 关联规则 /
- 事务数据库 /
- 预判筛选 /
- Apriori
Abstract: Association rule analysis, as one of the significant means of data mining, plays an important role in discovering the implicit knowledge in massive transaction data. To overcome the inherent defects of the classic Apriori algorithm, this paper proposes Apriori With Prejudging (AWP) algorithm. AWP algorithm adds a pre-judging procedure on the basis of the self-join and pruning progress in Apriori algorithm. It reduces and optimizes the k-frequent item sets using prior probability. In addition, the damping factor and compensating factor are introduced to revise the deviation caused by pre-judging. AWP algorithm simplifies the operation process of mining frequent item sets. Experimental results show that the improvement measures can effectively reduce the number of scanning databases and reduce the running time of the algorithm.
- Data mining /
- Association rules /
- Transaction database /
- Prejudging; Apriori /

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参考文献(19)

SINGLA S and MALIK A. Survey on various improved Apriori algorithms[J]. International Journal of Advanced Research in Computer and Communication Engineering, 2014, 3(11): 8528-8531. doi: 10.17148/ijarcce.2014.31139.

MINAL G I and SURYAVANSHI N Y. Association rule mining using improved Apriori algorithm[J]. International Journal of Computer Applications, 2015, 112(4): 37-42.

RAJESWARI K. Improved Apriori algorithm A comparative study using different objective measures[J]. International Journal of Computer Science and Information Technologies, 2015, 6(3): 3185-3191.

ACHAR A, LAXMAN S, and SASTRY P S. A unified view of the Apriori-based algorithms for frequent episode discovery[J]. Knowledge Information Systems, 2012, 31(2): 223-250. doi: 10.1007/s10115-011-0408-2.

李鹏, 于晓洋, 孙渤禹. 基于用户群组行为分析的视频推荐方法研究[J]. 电子与信息学报, 2014, 36(6): 1484-1491. doi: 10.3724/SP.J.1146.2013.01225.

LI Peng, YU Xiaoyang, and SUN Boyu. Video recommendation method based on group user behavior analysis[J]. Journal of Electronics Information Technology, 2014, 36(6): 1484-1491. doi: 10.3724/SP.J.1146.2013.01225.

AGRAWAL R and SRIKANT R. Fast algorithms for mining association rules[C]. VLDB94 Proceedings of the 20th International Conference on Very Large Data Bases, San Francisco, CA, USA, 1994: 487- 499.

YANG Z, TANG W, SHINTEMIROV A, et al. Association rule mining-based dissolved gas analysis for fault diagnosis of power transformers[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2009, 39(6): 597-610. doi: 10.1109/TSMCC.2009.2021989.

ZHANG F, ZHANG Y, and BAKOS J D. Gpapriori: Gpu-accelerated frequent itemset mining[C]. 2011 IEEE International Conference on Cluster Computing, Austin, TX, USA, 2011: 590-594. doi: 10.1109/CLUSTER.2011.61.

ANGELINE M D and JAMES S P. Association rule generation using Apriori mend algorithm for students placement[J]. International Journal of Emerging Sciences, 2012, 2(1): 78-86.

LI N, ZENG L, HE Q, et al. Parallel implementation of Apriori algorithm based on MapReduce[C]. 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel Distributed Computing (SNPD), Kyoto, Japan, 2012: 236-241. doi: 10.1109/ SNPD. 2012.31.

SULIANTA F, LIONG TH, and ATASTINA I. Mining food industrys multidimensional data to produce association rules using Apriori algorithm as a basis of business strategy[C]. 2013 International Conference of Information and Communication Technology (ICoICT), Bandung, Indonisia, 2013: 176-181. doi: 10.1109/ICoICT.2013.6574569.

ABAYA S A. Association rule mining based on Apriori algorithm in minimizing candidate generation[J]. International Journal of Scientific Engineering Research, 2012, 3(7): 1-4.

WANG Feng and LI Yonghua. An improved Apriori algorithm based on the matrix[C]. Proceedings of 2008 International Seminar on Future BioMedical Information Engineering, Wuhan, China, 2008: 152-155. doi: 10.1109/ FBIE.2008.80.

MAOLEGI M A and ARKOK B. An improved Apriori algorithm for association rules[J]. International Journal on Natural Language Computing, 2014, 3(1): 21-29. doi: 10.5121/ijnlc.2014.3103.

葛琳, 季新生, 江涛. 基于关联规则的网络信息内容安全事件发现及其Map-Reduce实现[J]. 电子与信息学报, 2014, 36(8): 1831-1837. doi: 10.3724/SP.J.1146.2013.01272.

GE Lin, JI Xinsheng, and JIANG Tao. Discovery of network information content security incidents based on association rules and its implementation in Map-Reduce[J]. Journal of Electronics Information Technology, 2014, 36(8): 1831-1837. doi: 10.3724/SP.J.1146.2013.01272.

TANK D M. Improved Apriori algorithm for mining association rules[J]. International Journal of Information Technology and Computer Science, 2014, 6(7): 15-23. doi: 10.5815/ijitcs.2014.07.03.

RAO S and GUPTA R. Implementing improved algorithm over Apriori data mining association rule algorithm[J]. International Journal of Computer Science and Technology, 2012, 34(3): 489-493.

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