基于领域知识和聚类的关联规则深层知识发现研究

doi:10.16381/j.cnki.issn1003-207x.2015.02.019

Abstract

Abstract: Second mining of the result of association rule mining is proposed in solution of the large numbers of redundant rules in the traditional association rules mining algorithm, and the algorithm for clustering of association rules is designed, then the novelty of the association rules is assessed after clustering based on the existing domain knowledge. It is insited that the association rules with more novelty and higher value can be stored in the domain knowledge base, and can be used for the decision or mining again. The algorithm proposed in this paper is effective to reduce the number of rules and also help to improve the novelty and precision of rule, which has a very high value for business applications. Finally the open source data from UCI is used to carry on the experiment to verify the effectiveness of the algorithm.

Key words: association rule, clustering, domain knowledge, post-processing

CLC Number:

ZHANG Ling-ling, ZHOU Quan-liang, TANG Guang-wen, LI Xing-sen, SHI Yong. Research on Algorithm of Post-processing Association Rules Based on Clustering and Domain Knowledge[J]. Chinese Journal of Management Science, 2015, 23(2): 154-161.

References

[1] 马建庆,钟亦平,张世永.基于兴趣度的关联规则挖掘算法[J].计算机工程,2006,32(17),121-122.

[2] 朱恒民,姬小利,王宁生.一种挖掘意外规则的方法[J].南京航空航天大学学报,2005,37(3):381-385.

[3] 韦素云,吉根林,曲维光.关联规则的冗余删除与聚类[J].小型微型计算机系统,2006,27(1):110-113.

[4] 杨立波.基于聚类的关联规则挖掘算法[J].太原大学学报,2011,12(3):113-116.

[5] 朱正祥.领域驱动知识发现方法研究[D].大连:大连理工大学,2010.

[6] 李军.智能知识管理模型与获取算法研究[D].北京:中国科学院研究生院,2011.

[7] 朱靖波,陈文亮。基于领域知识的文本分类[J].东北大学学报(自然科学版),2005,26(8):733-735。

[8] 杨立.基于领域知识的知识发现研究[D].北京:中国科学院软件研究所,2005.

[9] 张文凌.领域知识参与数据挖掘预处理阶段的研究[D].北京:北京工业大学,2006.

[10] 朱恒民.领域知识制导的数据挖掘技术及其在中药提取中的应用[D].南京:南京航空航天大学,2006.

[11] 莫富强.基于领域知识的贝叶斯网络学习研究[D].合肥:合肥工业大学,2008.

[12] Hand D,Mannila H,Smyth P.数据挖掘原理[M].张银奎,廖丽,宋俊,译.北京:机械工业出版社,2003.

[13] Lavrac N, Flach P, Zupan B. Rule evaluation measures: A unifying view[C]. Proceedings of the Ninth International Workshop on Inductive Logic Programming, Bled, Slovenia,June 24-27,1999.

[14] Agrawal R,Imielinski T,Swami A. Mining association rules between sets of items in large databases[C]. Proceedings of the ACM SIGMOD Conference on Management of Data,Washington,DC,May 26-28,1993.

[15] Ludwig J, Livingstone G. What's new using prior models as a measure of novelty in knowledge discovery[C]. Proceedings of the 24th IEEE Conference on Tools with Artificial Intelligence,Athens,November 7-9,2012.

[16] Silberschatz A,Tuzhilin A.What makes patterns interesting in knowledge discovery systems[J]. IEEE Trans Transactions on. Knowledge and Data Engineering, 1996,8(6):970-974.

[17] Freitas A.On rule interestingness measures[J]. Knowledge Based Systems,1999,12(5):309-315.

[18] Geng Liqiang, Hamilton H J. Choosing the right lens: Finding what is interesting in data mining[M]//Guillet F,Hamilton H J. Quality measures in data mining. Berlin Heidelberg: Springer, 2007: 3-24.

[19] Hilderman R J, Hamilton H J. Measuring the interestingness of discovered knowledge: A principled approach[J]. Intelligent Data Analysis, 2003, 7(4): 347-382.

[20] Guillet F, Hamilton H J. Quality measures in data mining[M]. Berlin: Springer, 2007.

[21] Dong Guozhu, Li Jinyan. Interestingness of discovered association rules in terms of neighborhood-based unexpectedness[M]//Wu Xinding,Kotagiri R,Korb K B. Research and development in knowledge discovery and data mining. Berlin Heidelberg: Springer, 1998: 72-86.

[22] Lu Songfeng, Hu Heping, Li Fan. Mining weighted association rules[J]. Intelligent Data Analysis, 2001, 5(3): 211-225.

[23] Shen Yidong, Zhang Zhong, Yang Qiang. Objective-oriented utility-based association mining[C]. Proceedings of the IEEE International Conference on Data Mining,Maebashi City,Japan,December 9-12,2002.

[24] Yao Hong, Hamilton H J, Butz C J. A foundational approach to mining itemset utilities from databases[C]. Proceedings of the 2004 SIAM International Conference on Data Mining,Florida,April 22-24,2004.

[25] Ling C X, Chen Tielin, Yang Qiang, et al. Mining optimal actions for profitable CRM[C]. Proceedings of the IEEE International Conference on Data Mining,Maebashi City,Japan,December 9-12.2002.

[26] Wang Ke, Zhou Sengjang, Han Jianwei. Profit mining: From patterns to actions[M]//Bertion E,christodoulakiss,Plexousakis D. Advances in Database Technology. Berlin Heidelberg: Springer, 2002: 70-87.

Research on Algorithm of Post-processing Association Rules Based on Clustering and Domain Knowledge

PDF (PC)

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 10

[1]	WANG Ze-zhou, CHEN Yun-xiang, XIANG Hua-chun. Research on an Improved Expert Cluster Weighting Method Based on Fuzzy Kernel Clustering [J]. Chinese Journal of Management Science, 2021, 29(2): 177-183.
[2]	FANG Zhi-geng, GENG Sun-yue, LIU Si-feng, GAO Su, XIE Shi-kun. Clustering Structure Cooperative Working Mechanism Design of Low Orbit Satellite Communication Network Based on Spherical Coordinate System [J]. Chinese Journal of Management Science, 2021, 29(2): 195-204.
[3]	CHEN Heng, LU Wei, DU Lei. Stochastic Evolutionary Scenario Analysis of Risk Set Clustering NIMBY Conflicts Events [J]. Chinese Journal of Management Science, 2020, 28(4): 131-141.
[4]	SUN Qi, JI Shou-feng, DONG Ming. Optimization of Distribution Service Quality with Constraints of Quality Preference for Data-driven Model [J]. Chinese Journal of Management Science, 2019, 27(6): 41-52.
[5]	LV Tong, SHI Le-feng, ZOU Xiao-yan. Two-Step Difference Clustering-Firefly Algorithm for Sharing Commercial Network Development with Network Effect [J]. Chinese Journal of Management Science, 2019, 27(6): 146-157.
[6]	WU Zi-xuan, LI Tie-ke, ZHANG Wen-xin, WANG Bai-lin, Wang Jian-jian. Order Scheduling Model and Algorithm for Hot-Rolled Seamless Steel Tube with Delivery Requirements [J]. Chinese Journal of Management Science, 2018, 26(5): 129-137.
[7]	SUN Xiao-lin, JIN Chun, MA Lin, WANG Wen-bo. Supplier Matching Method Based on Ontology and Fuzzy QoS in Cloud Manufacturing Environments [J]. Chinese Journal of Management Science, 2018, 26(1): 128-138.
[8]	LI Jin, LI Hong, XU Li-Li, WANG Hua. Research on Assembly Line Scheduling Problem Based on Improved Genetic Algorithm [J]. Chinese Journal of Management Science, 2016, 24(12): 63-71.
[9]	CHEN Yun-xiang, DONG Xiao-xiong, XIANG Hua-chun, CAI Zhong-yi. Method for Combination Weighting Experts Based on Information Entropy and Cluster Analysis [J]. Chinese Journal of Management Science, 2015, 23(6): 142-146.
[10]	JI Ling, NIU Dong-xiao, WANG Peng. Photovoltaic Load Forecasting Based on the Similar Day and Bayesian Neural Network [J]. Chinese Journal of Management Science, 2015, 23(3): 118-122.
[11]	SHAN Hai-yan, WANG Wen-ping. Analysis of the Structure of Inter-organization Innovation Network during the Process of Knowledge Integration [J]. Chinese Journal of Management Science, 2012, 20(6): 176-184.
[12]	CHENG Yan-qiu, CHI Guo-tai. The Ecosystem Evaluation Model Based on Kernel Principle Component Analysis and Its Applications [J]. Chinese Journal of Management Science, 2011, 19(3): 182-192.
[13]	SHI Qin, WANG Nan-nan, QIU Duo-yang. Application of Fuzzy Clustering Based on Particle Swarm Optimization in Vehicle Driving Cycle [J]. Chinese Journal of Management Science, 2011, 19(2): 110-115.
[14]	CHI Guo-tai, CHENG Yan-qiu, WANG Li-jun. The Society Evaluation Model Based on Grey Clustering and Its Empirical Study of Cities under Province [J]. Chinese Journal of Management Science, 2010, 18(6): 185-192.
[15]	ZENG Min-gang, CUI Zeng-shou, YU Gao-hui. Research on Location-Routing Problem of Relief System Based on Emergency Logistics [J]. Chinese Journal of Management Science, 2010, 18(2): 75-80.