基于数据挖掘的入侵检测方法研究
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摘要
随着IT技术的迅速发展,信息安全问题成为了人们关注的一个焦点,其中数据库系统的安全问题是信息安全中非常重要的一个部分。数据挖掘作为一种规则挖掘手段被引入到了入侵检测中。近年来,不少专家、学者分别就如何提高入侵检测系统规则挖掘速度展开了研究并取得了一定成果。
本文对经典的数据挖掘算法Apriori算法进行改进,将其引入至入侵检测系统。主要研究工作如下:
(1)提出了基于改进Apriori算法的数据库入侵检测系统,该系统共包含数据预处理模块、规则生成模块、入侵检测模块和响应模块四大模块。在数据预处理模块中,首先将审计数据转换为布尔型数据以便进行规则挖掘;在规则生成模块中,引入改进之后的Apriori算法以提升规则挖掘效率;在入侵检测模块中,使用异常检测与滥用检测相结合的复合式搜索引擎以克服二者的缺点;在响应模块中,对不同的检测结果分别进行响应。
(2)对经典的Apriori算法进行研究,针对Apriori算法做联合规则挖掘时,需要为每一个候选项集C的成员扫描整个数据库来确定其支持度的特点,提出一种改进的Apriori算法,该算法在使用Apriori性质删除候选项集时,采用频繁子集数量多少的判别方式减少对上级频繁项集的扫描。在计算候选项集支持度时,采用索引树数据结构存储数据,从而大大降低了对数据库的扫描次数,提高算法的整体效率。并将改进后的Apriori算法应用至审计数据规则挖掘。
Along with the fast development of the Information Technology, the security problem of information becomes the focus of people’s attention, and the security problem of the database system is a very important part of the information security. Data mining as a rule mining method is introduced in intrusion detection. In recent years, many experts and academicians have researched in how to enhance the efficiency of the rule mining of the intrusion detection system, and have acquired many achievements.
This paper has improved on the classic data mining algorithm Apriori, and introduces it to intrusion detection system. The main researches are showed below.
(1)This paper put forward a database intrusion detection system based on the advanced Apriori algorithm. This system includes data preprocessing module, rule creating module, intrusion detection module and response module. In the data preprocessing module, we first transform the audit data to the boolean data in order to do the rule mining; In the rule creating module, we introduce the advanced Apriori algorithm in order to enhance the efficiency of the rule mining; In the intrusion detection module, we use compound search engine composition by abnormity detection and the abuse detection, in order to overcome their weakness; In the response module, it can response respectively to the different result.
(2)This paper research in the classic Apriori algorithm, aiming at the Apriori algorithm’s characteristic that it have to scan the whole databace for each member of candidate itemset C to define the degree of support, we introduce an advanced Apriori algorithm, it will adopt distinction of number of frequent subitem to decreace the scan of the superior frequent items when this algorithm use Apriori property to delete the candidate itemset. When it compute the degree of support of the candidate items,it adopt the data structure of index tree to store data, which can decreace the scan of databace to a large extent and raise the algorithm’s whole efficiency. And it introduces the advanced Apriori algorithm to the audit data rule mining.
本文对经典的数据挖掘算法Apriori算法进行改进,将其引入至入侵检测系统。主要研究工作如下:
(1)提出了基于改进Apriori算法的数据库入侵检测系统,该系统共包含数据预处理模块、规则生成模块、入侵检测模块和响应模块四大模块。在数据预处理模块中,首先将审计数据转换为布尔型数据以便进行规则挖掘;在规则生成模块中,引入改进之后的Apriori算法以提升规则挖掘效率;在入侵检测模块中,使用异常检测与滥用检测相结合的复合式搜索引擎以克服二者的缺点;在响应模块中,对不同的检测结果分别进行响应。
(2)对经典的Apriori算法进行研究,针对Apriori算法做联合规则挖掘时,需要为每一个候选项集C的成员扫描整个数据库来确定其支持度的特点,提出一种改进的Apriori算法,该算法在使用Apriori性质删除候选项集时,采用频繁子集数量多少的判别方式减少对上级频繁项集的扫描。在计算候选项集支持度时,采用索引树数据结构存储数据,从而大大降低了对数据库的扫描次数,提高算法的整体效率。并将改进后的Apriori算法应用至审计数据规则挖掘。
Along with the fast development of the Information Technology, the security problem of information becomes the focus of people’s attention, and the security problem of the database system is a very important part of the information security. Data mining as a rule mining method is introduced in intrusion detection. In recent years, many experts and academicians have researched in how to enhance the efficiency of the rule mining of the intrusion detection system, and have acquired many achievements.
This paper has improved on the classic data mining algorithm Apriori, and introduces it to intrusion detection system. The main researches are showed below.
(1)This paper put forward a database intrusion detection system based on the advanced Apriori algorithm. This system includes data preprocessing module, rule creating module, intrusion detection module and response module. In the data preprocessing module, we first transform the audit data to the boolean data in order to do the rule mining; In the rule creating module, we introduce the advanced Apriori algorithm in order to enhance the efficiency of the rule mining; In the intrusion detection module, we use compound search engine composition by abnormity detection and the abuse detection, in order to overcome their weakness; In the response module, it can response respectively to the different result.
(2)This paper research in the classic Apriori algorithm, aiming at the Apriori algorithm’s characteristic that it have to scan the whole databace for each member of candidate itemset C to define the degree of support, we introduce an advanced Apriori algorithm, it will adopt distinction of number of frequent subitem to decreace the scan of the superior frequent items when this algorithm use Apriori property to delete the candidate itemset. When it compute the degree of support of the candidate items,it adopt the data structure of index tree to store data, which can decreace the scan of databace to a large extent and raise the algorithm’s whole efficiency. And it introduces the advanced Apriori algorithm to the audit data rule mining.
引文
[1]胡道元,阂京华.网络安全[M].北京:清华大学出版社,2004,7
[2]石少敏.基于数据挖掘的混合式入侵检测模型及分析[J].通信技术,2009,(08):42-47
[3]傅德胜,周舒,郭萍.基于数据挖掘的分布式网络入侵检测系统设计及实现[J].北京:计算机科学,2009,(03):243-249
[4]张亚玲,康立锦.基于数据挖掘的Snort系统改进模型[J].计算机应用,2009,(02):146-150
[5]Thuraisingham,Bhavani.Data Mining for Malicious Code Detection And Security Applications[J],Web Intelligence and Intelligent Agent Technologies,2009
[6]Ming Xue,Changjun Zhou.Applied Research on Data Mining Algorithm in Network Intrusion Detection[J],Artificial Intelligence,2009
[7]WenkeLee, Stolfo, S.J,chan, P.k; Eskin,E.; WeiFan, Miller, M.Hershkop,S., Junxin zhang; Real time data mining-based intrusion detection[J],DARPA Information Survivability Conference&Exposition II,2001
[8]何海涛,吕士勇,田海燕.基于改进Apriori算法的数据库入侵检测[J].计算机工程,2009,(06):67-73
[9]袁朝华,柏文阳.一种数据库入侵检测模型的研究[J].计算机应用研究, 2006, (07):127-131
[10]王静,易军凯.基于入侵检测的数据库安全模型研究[J].微计算机信息. 2006,(03):684-689
[11]王文娟,王杰,李冬梅,杜光辉.基于Apriori改进算法的入侵检测系统的研究[J].微计算机信息. 2009,(03):187-192
[12]Yun Wang,Weihuang Fu,Agrawal;D.P. Intrusion detection in Gaussian distributed Wireless Sensor Networks , Mobile Adhoc and Sensor Systems.in MASS '09. IEEE 6th International Conference on . 2009 , 313– 321
[13]Yongquan Mo, Yizhong Ma, Liang Xu, Design and implementation of intrusion detection based on mobile agents . IT in Medicine and Education, 2008. ITME 2008. IEEE International Symposium on . 2008 , Page(s): 278– 281
[14]Yunlu Gong, Mabu, S., Ci Chen, Yifei Wang; Hirasawa, K., Intrusion detection system combining misuse detection and anomaly detection using Genetic NetworkProgramming . Machine Learning and Cybernetics, 2009 International Conference on. 2009 , Page(s): 3463– 3467
[15]Yu-Xin Ding, Min Xiao, Ai-Wu Liu, Research and implementation on snort-based hybrid intrusion detection system . Machine Learning and Cybernetics, 2009 International Conference on . 2009 , Page(s): 1414– 1418
[16]Cuixiao Zhang, Guobing Zhang, Shanshan Sun, A Mixed Unsupervised Clustering-Based Intrusion Detection Model . Genetic and Evolutionary Computing, WGEC '09. 3rd International Conference on 2009 , Page(s): 1154– 1159
[17]Xiaohui Bao, Tianqi Xu, Hui Hou, Network Intrusion Detection Based on Support Vector Machine . Management and Service Science, 2009.
[18]Meijuan Gao, Jingwen Tian, Mingping Xia,Intrusion Detection Method Based on Classify Support Vector Machine . Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
[19]Xiaohong Qu, Zhijie Liu, Xiaoyao Xie, Research on distributed intrusion detection system based on Protocol analysis . Anti-counterfeiting, Security, and Identification in Communication, 2009. ASID 2009. 3rd International Conference on
[20]Ming Xue, Changjun Zhu, Applied Research on Data Mining Algorithm in Network Intrusion Detection . Artificial Intelligence, 2009. JCAI '09. International Joint Conference on
[21]Li Min, Wang Dongliang, Anormaly Intrusion Detection Based on SOM . Information Engineering, 2009. ICIE '09. WASE International Conference on
[22]Wei Wang, Xiangliang Zhang, Gombault, S., Knapskog, S.J., Attribute Normalization in Network Intrusion Detection . Pervasive Systems, Algorithms, and Networks (ISPAN), 2009 10th International Symposium on , Page(s): 448– 453
[23]Sang Min Lee, Dong Seong Kim, YoungHyun Yoon, Jong Sou Park, Quantitative Intrusion Intensity Assessment Using Important Feature Selection and Proximity Metrics , Dependable Computing, 2009. PRDC '09. 15th IEEE Pacific Rim International Symposium on Page(s): 127 - 134
[24]Liu Jing, Lu Yongquan, Wang Jintao, Gao Pengdong, Qiu Chu, Ji Haipeng, Li Nan, Yu Wenhua, An improved apriori algorithm for early warning of equipment failue . Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on . Page(s): 450– 452
[25]Changsheng Zhang, Jing Ruan, A Modified Apriori Algorithm with Its Application in Instituting Cross-Selling Strategies of the Retail Industry . Electronic Commerce andBusiness Intelligence, 2009. ECBI 2009. International Conference on , Page(s): 515– 518
[26]Shah, K.D., Mahajan, S., Performance Analysis of Parallel Apriori on Heterogeneous Nodes . Advances in Computing, Control, & Telecommunication Technologies, 2009. ACT '09. International Conference on .Page(s): 42– 44
[27]Qihua Lan, Defu Zhang, Bo Wu, A New Algorithm for Frequent Itemsets Mining Based on Apriori and FP-tree . Intelligent Systems, 2009. GCIS '09. WRI Global Congress on .Volume: 2, Page(s): 360– 364.
[28]Pei-ji Wang, Lin Shi, Jin-niu Bai, Yu-lin Zhao, Mining Association Rules Based on Apriori Algorithm and Application . Computer Science-Technology and Applications, 2009. IFCSTA '09. International Forum on . Page(s): 141– 143
[29]Huan Wu,Zhigang Lu, Lin Pan, Rongsheng Xu, Wenbao Jiang, An Improved Apriori-based Algorithm for Association Rules Mining . Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on , Page(s): 51– 55
[30]Yan-hua Wang, Xia Feng,The Optimization of Apriori Algorithm Based on Directed Network . Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on . Page(s): 504– 507.
[31]Zhang Changsheng, Li Zhongyue, Zheng Dongsong,An Improved Algorithm for Apriori . Education Technology and Computer Science, 2009. ETCS '09. First International Workshop on . Page(s): 995– 998.
[32]Wei Yong-qing, Yang Ren-hua, Liu Pei-yu,An improved Apriori algorithm for association rules of mining . IT in Medicine & Education, 2009. ITIME '09. IEEE International Symposium on , Page(s): 942– 946
[33]Gang Yang, Hong Zhao; Lei Wang, Ying Liu,An implementation of improved apriori algorithm . Machine Learning and Cybernetics, 2009 International Conference on. Page(s): 1565 - 1569
[34]Pan Qingxian, Qu Linjie, Lou Lanfang, Data mining and application of teaching evaluation based on association rules . Computer Science & Education, 2009. ICCSE '09. 4th International Conference on , Page(s): 1404– 1407
[35]Umarani, V., Punithavalli, M., Developing Novel and Effective Approach for Association Rule Mining Using Progressive Sampling . Computer and Electrical Engineering, 2009. ICCEE '09. Second International Conference on . Page(s): 610– 614.
[36]徐林章,赵强,张艳宁.一种基于FP_Tree算法的FP-树构造方法[M].西安:计算机工程,2009
[37]鲁为,王枞. FP树算法的优化与比较[J].计算机工程,2007, 33(16) 189-190
[38] Han Jiawei, Pei Jian, Yin Yiwen. Mining Frequent Patterns WithoutCandidate Generation[C]//Proc. of ACM-SIGMOD Int’l Conf. onManagement of Data. Dallas, USA: ACM Press, 2000.
[39]宋余庆,朱玉全,孙志挥.等.基于FP_Tree的最大频繁项目集挖掘及更新算法[J].软件学报, 2003, 14(9): 1586-1592
[40]郭宇红,童云海,唐世渭.等.基于FP_Tree的反向频繁项集挖掘[J].软件学报, 2008, 19(2): 338-350.
[41]田大新,刘衍珩,魏达. ARTNIDS:基于自适应谐振理论的网络入侵检测系统[J].计算机学报, 2005, 28(11): 1882-1889
[42]刘军,艾力斯木吐拉,段天山,刘平仁,马宝柱.一种改进的Apriori挖掘算法的研究与应用[J].武汉理工大学学报, 2009, (19): 265-269
[43]欧阳为民,蔡庆生.国际关联规则发现研究评述[J].计算机科学,1993.(3):41-44
[44]季伟东,张珑,张军.一种Apriori算法的改进[J].计算机工程与科学,2009, (09): 476-480
[45]Shengwei Li, Lingsheng Li,Chong Han. Mining closed frequent itemset based on FP-tree . Granular Computing, 2009, GRC '09. IEEE International Conference on . Page(s): 354– 357
[46]Qihua Lan, Defu Zhang, Bo Wu. A New Algorithm for Frequent Itemsets Mining Based on Apriori and FP-tree . Intelligent Systems, 2009. GCIS '09. WRI Global Congress on . Page(s): 360– 364
[47]Min Chen,XueDong Gao, HuiFei Li. An efficient parallel FP-Growth algorithm , Cyber-Enabled Distributed Computing and Knowledge Discovery, 2009. CyberC '09. International Conference on . Page(s): 283– 286
[48]Ding Zhenguo, Wei Qinqin, Ding Xianhua. An Improved FP-Growth Algorithm Based on Compound Single Linked List . Information and Computing Science, 2009. ICIC '09. Second International Conference on Page(s): 351– 353
[49]Ye-In Chang, Chen-Chang Wu,Jun-Hong Shen.Chien-Hung Chen; Data Classification Based on the Class-Rooted FP-tree Approach . Complex, Intelligent and Software IntensiveSystems, 2009. CISIS '09. International Conference on . Page(s): 506– 511
[50]Agrawal R,Mielinski T and A.Swami,Mining Association Rules Between Set of Items in large Database.Proceeding of ACIFSIG,1990 International Conference. (1993),207-216
[51]王丽娜,董晓梅,郭晓淳,于戈.基于数据挖掘的网络数据库入侵检测系统[J].东北大学学报,2003,24(3):225-228
[52]Chunfu Jia, Deqiang Chen, Performance Evaluation of a Collaborative Intrusion Detection System , Natural Computation, 2009. ICNC '09. Fifth International Conference on Volume: 6, 2009 , Page(s): 409– 413
[53]Jun Tan, Yingyong Bu,Bo Yang. An Efficient Close Frequent Pattern Mining Algorithm . Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on .Page(s): 528 - 531
[2]石少敏.基于数据挖掘的混合式入侵检测模型及分析[J].通信技术,2009,(08):42-47
[3]傅德胜,周舒,郭萍.基于数据挖掘的分布式网络入侵检测系统设计及实现[J].北京:计算机科学,2009,(03):243-249
[4]张亚玲,康立锦.基于数据挖掘的Snort系统改进模型[J].计算机应用,2009,(02):146-150
[5]Thuraisingham,Bhavani.Data Mining for Malicious Code Detection And Security Applications[J],Web Intelligence and Intelligent Agent Technologies,2009
[6]Ming Xue,Changjun Zhou.Applied Research on Data Mining Algorithm in Network Intrusion Detection[J],Artificial Intelligence,2009
[7]WenkeLee, Stolfo, S.J,chan, P.k; Eskin,E.; WeiFan, Miller, M.Hershkop,S., Junxin zhang; Real time data mining-based intrusion detection[J],DARPA Information Survivability Conference&Exposition II,2001
[8]何海涛,吕士勇,田海燕.基于改进Apriori算法的数据库入侵检测[J].计算机工程,2009,(06):67-73
[9]袁朝华,柏文阳.一种数据库入侵检测模型的研究[J].计算机应用研究, 2006, (07):127-131
[10]王静,易军凯.基于入侵检测的数据库安全模型研究[J].微计算机信息. 2006,(03):684-689
[11]王文娟,王杰,李冬梅,杜光辉.基于Apriori改进算法的入侵检测系统的研究[J].微计算机信息. 2009,(03):187-192
[12]Yun Wang,Weihuang Fu,Agrawal;D.P. Intrusion detection in Gaussian distributed Wireless Sensor Networks , Mobile Adhoc and Sensor Systems.in MASS '09. IEEE 6th International Conference on . 2009 , 313– 321
[13]Yongquan Mo, Yizhong Ma, Liang Xu, Design and implementation of intrusion detection based on mobile agents . IT in Medicine and Education, 2008. ITME 2008. IEEE International Symposium on . 2008 , Page(s): 278– 281
[14]Yunlu Gong, Mabu, S., Ci Chen, Yifei Wang; Hirasawa, K., Intrusion detection system combining misuse detection and anomaly detection using Genetic NetworkProgramming . Machine Learning and Cybernetics, 2009 International Conference on. 2009 , Page(s): 3463– 3467
[15]Yu-Xin Ding, Min Xiao, Ai-Wu Liu, Research and implementation on snort-based hybrid intrusion detection system . Machine Learning and Cybernetics, 2009 International Conference on . 2009 , Page(s): 1414– 1418
[16]Cuixiao Zhang, Guobing Zhang, Shanshan Sun, A Mixed Unsupervised Clustering-Based Intrusion Detection Model . Genetic and Evolutionary Computing, WGEC '09. 3rd International Conference on 2009 , Page(s): 1154– 1159
[17]Xiaohui Bao, Tianqi Xu, Hui Hou, Network Intrusion Detection Based on Support Vector Machine . Management and Service Science, 2009.
[18]Meijuan Gao, Jingwen Tian, Mingping Xia,Intrusion Detection Method Based on Classify Support Vector Machine . Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
[19]Xiaohong Qu, Zhijie Liu, Xiaoyao Xie, Research on distributed intrusion detection system based on Protocol analysis . Anti-counterfeiting, Security, and Identification in Communication, 2009. ASID 2009. 3rd International Conference on
[20]Ming Xue, Changjun Zhu, Applied Research on Data Mining Algorithm in Network Intrusion Detection . Artificial Intelligence, 2009. JCAI '09. International Joint Conference on
[21]Li Min, Wang Dongliang, Anormaly Intrusion Detection Based on SOM . Information Engineering, 2009. ICIE '09. WASE International Conference on
[22]Wei Wang, Xiangliang Zhang, Gombault, S., Knapskog, S.J., Attribute Normalization in Network Intrusion Detection . Pervasive Systems, Algorithms, and Networks (ISPAN), 2009 10th International Symposium on , Page(s): 448– 453
[23]Sang Min Lee, Dong Seong Kim, YoungHyun Yoon, Jong Sou Park, Quantitative Intrusion Intensity Assessment Using Important Feature Selection and Proximity Metrics , Dependable Computing, 2009. PRDC '09. 15th IEEE Pacific Rim International Symposium on Page(s): 127 - 134
[24]Liu Jing, Lu Yongquan, Wang Jintao, Gao Pengdong, Qiu Chu, Ji Haipeng, Li Nan, Yu Wenhua, An improved apriori algorithm for early warning of equipment failue . Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on . Page(s): 450– 452
[25]Changsheng Zhang, Jing Ruan, A Modified Apriori Algorithm with Its Application in Instituting Cross-Selling Strategies of the Retail Industry . Electronic Commerce andBusiness Intelligence, 2009. ECBI 2009. International Conference on , Page(s): 515– 518
[26]Shah, K.D., Mahajan, S., Performance Analysis of Parallel Apriori on Heterogeneous Nodes . Advances in Computing, Control, & Telecommunication Technologies, 2009. ACT '09. International Conference on .Page(s): 42– 44
[27]Qihua Lan, Defu Zhang, Bo Wu, A New Algorithm for Frequent Itemsets Mining Based on Apriori and FP-tree . Intelligent Systems, 2009. GCIS '09. WRI Global Congress on .Volume: 2, Page(s): 360– 364.
[28]Pei-ji Wang, Lin Shi, Jin-niu Bai, Yu-lin Zhao, Mining Association Rules Based on Apriori Algorithm and Application . Computer Science-Technology and Applications, 2009. IFCSTA '09. International Forum on . Page(s): 141– 143
[29]Huan Wu,Zhigang Lu, Lin Pan, Rongsheng Xu, Wenbao Jiang, An Improved Apriori-based Algorithm for Association Rules Mining . Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on , Page(s): 51– 55
[30]Yan-hua Wang, Xia Feng,The Optimization of Apriori Algorithm Based on Directed Network . Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on . Page(s): 504– 507.
[31]Zhang Changsheng, Li Zhongyue, Zheng Dongsong,An Improved Algorithm for Apriori . Education Technology and Computer Science, 2009. ETCS '09. First International Workshop on . Page(s): 995– 998.
[32]Wei Yong-qing, Yang Ren-hua, Liu Pei-yu,An improved Apriori algorithm for association rules of mining . IT in Medicine & Education, 2009. ITIME '09. IEEE International Symposium on , Page(s): 942– 946
[33]Gang Yang, Hong Zhao; Lei Wang, Ying Liu,An implementation of improved apriori algorithm . Machine Learning and Cybernetics, 2009 International Conference on. Page(s): 1565 - 1569
[34]Pan Qingxian, Qu Linjie, Lou Lanfang, Data mining and application of teaching evaluation based on association rules . Computer Science & Education, 2009. ICCSE '09. 4th International Conference on , Page(s): 1404– 1407
[35]Umarani, V., Punithavalli, M., Developing Novel and Effective Approach for Association Rule Mining Using Progressive Sampling . Computer and Electrical Engineering, 2009. ICCEE '09. Second International Conference on . Page(s): 610– 614.
[36]徐林章,赵强,张艳宁.一种基于FP_Tree算法的FP-树构造方法[M].西安:计算机工程,2009
[37]鲁为,王枞. FP树算法的优化与比较[J].计算机工程,2007, 33(16) 189-190
[38] Han Jiawei, Pei Jian, Yin Yiwen. Mining Frequent Patterns WithoutCandidate Generation[C]//Proc. of ACM-SIGMOD Int’l Conf. onManagement of Data. Dallas, USA: ACM Press, 2000.
[39]宋余庆,朱玉全,孙志挥.等.基于FP_Tree的最大频繁项目集挖掘及更新算法[J].软件学报, 2003, 14(9): 1586-1592
[40]郭宇红,童云海,唐世渭.等.基于FP_Tree的反向频繁项集挖掘[J].软件学报, 2008, 19(2): 338-350.
[41]田大新,刘衍珩,魏达. ARTNIDS:基于自适应谐振理论的网络入侵检测系统[J].计算机学报, 2005, 28(11): 1882-1889
[42]刘军,艾力斯木吐拉,段天山,刘平仁,马宝柱.一种改进的Apriori挖掘算法的研究与应用[J].武汉理工大学学报, 2009, (19): 265-269
[43]欧阳为民,蔡庆生.国际关联规则发现研究评述[J].计算机科学,1993.(3):41-44
[44]季伟东,张珑,张军.一种Apriori算法的改进[J].计算机工程与科学,2009, (09): 476-480
[45]Shengwei Li, Lingsheng Li,Chong Han. Mining closed frequent itemset based on FP-tree . Granular Computing, 2009, GRC '09. IEEE International Conference on . Page(s): 354– 357
[46]Qihua Lan, Defu Zhang, Bo Wu. A New Algorithm for Frequent Itemsets Mining Based on Apriori and FP-tree . Intelligent Systems, 2009. GCIS '09. WRI Global Congress on . Page(s): 360– 364
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