Download presentation
Presentation is loading. Please wait.
1
关联分类算法的研究 符号学习研究组 赵东垒 Hebei University
2
课题研究目的 国际研究现状 主要研究内容和创新点 研究过程可能遇到的困难及解决方案 总结 参考文献 Hebei University
3
课题研究目的 分类问题是通过分析给定的一个带有类别标识的训练数据集，建立一个分类器，然后预测那些未知类别的数据对象 关联分类算法
数据集中属性的取值是符号型的 课题研究目的就是改进、优化关联分类算法 提高关联分类算法的分类精度 提高关联分类算法的效率 提高关联分类算法的可理解性 Hebei University
4
国际研究现状 1998年Liu等提出了基于类关联规则的分类算法CBA。 1999年Dong等提出显露模式分类法CAEP。
2000年Wang等结合关联规则分类和决策树分类提出关联决策树。 2001年Li等提出基于多条关联规则的分类算法CMAR。 2003年Yin等提出预测型关联规则的分类算法CPAR。CPAR采用贪婪方法从数据集中挖掘出较小规则集。 2004年Antonie提出正负关联规则的分类算法。 2005年Wang提出HARMONY，它直接挖掘覆盖样例置信度最高的规则。 2006年Adriano Veloso等提出的lazy关联分类。 2006，2007年Arunasalam提出了适用与类不平衡数据上的关联分类。 Hebei University
5
基本概念 关联规则：A=>B If A then C 定义1 规则的支持度 定义2 规则的置信度
数据集中匹配规则前件A, 并且满足类别属性取值为C的样例的个数. 定义2 规则的置信度 规则的支持度与数据集中匹配规则前件A的样例的个数的比值. Hebei University
6
主要研究内容和创新点 关联分类算法的优点 关联分类算法存在的问题 分类精度高 适应性强 算法的执行效率 更高效的挖掘方法
算法的执行效率 更高效的挖掘方法 剪枝的质量和效率 新的规则序关系 分类器的可理解性 交叠现象对分类起的影响 Hebei University
7
已完成的工作 算法的执行效率 在构造带类别标识的FP-tree时，在每个节点注册相应类别信息。
扩展TD-FP-Growth算法，使它能直接挖掘满足最小支持度和最小置信度的类关联规则。 优点：两次扫描数据库，不用重复建立条件FP-tree。减少了内存消耗，提高了运行效率。 Hebei University
8
带类别标识FP-tree的构造 Hebei University
9
剪枝的质量和效率 关联分类中最敏感的问题 如何评价类关联规则的质量 如何从大量的关联规则中选择有效的规则构造分类器
Hebei University
10
如何评价类关联规则的质量 经典关联分类规则序关系的定义 给定规则Ri，Rj。 Ri优于Rj，当且仅当满足以下条件之一:
Hebei University
11
经典关联分类规则序关系的缺点 综合考虑置信度和支持度。
其本质是采用置信度，支持度，规则项数目评价顺序。过分强调了置信度，这样在最后构造的分类器中，使得有些规则置信度很高而支持度不高，造成过度拟合。 综合考虑置信度和支持度。 Hebei University
12
R1有较好的泛化能力，R2可能过度拟合数据。
R1: sup(R1) = 100, conf(R1) = 98% R2: sup(R2) = 10, conf(R2) = 100% 经典序关系 R1 < R2 R1 > R2 R1有较好的泛化能力，R2可能过度拟合数据。 Hebei University
13
15个UCI数据库测试结果 Hebei University
14
医疗图像数据库测试结果 Hebei University
15
以后要完成的工作 完善规则评价函数 引入规则的项数 考虑类别不平衡情况 分类器中规则交叠对分类精度的影响 Hebei University
16
分类器的可理解性 关联分类构造分类器的方法 分类器的特点 挖掘满足置信度和支持度阈值要求的类关联规则
将规则按定义的序关系排序，基于数据覆盖来选择规则 分类器的特点 数据集中每条记录都被一条评价值最高的规则覆盖 分类器中的规则在训练集中存在相互交叠的现象 规则的数目较多 Hebei University
17
交叠现象怎样产生的 1 .. 10 20 30 40 Hebei University R1:20, 100% R2:20, 95%
18
交叠问题解决方法 每选择一条规则后，更新剩余规则的置信度，支持度。 难度 更新的计算量大 采用更新，是否比以前的方法有效
Hebei University
19
研究过程可能遇到的困难及解决方案 规则评价函数的确定 交叠现象对分类精度的影响 不同数据库的影响 选择规则后，更新置信度和支持度
比较不同交叠情况的分类精度 Hebei University
20
总结 针对关联分类算法存在的问题 算法的执行效率 剪枝的质量和效率 分类器的可理解性 Hebei University
21
参考文献 [1] B. Liu, W. Hsu and Y. Ma. Integrating Classification and Association Rule Mining. In Proc. of 1998 Int. Conf. on Knowledge Discovery and Data Mining (KDD'98), pp.80-86, New York, Aug 1998. [2] J. Han, J. Pei and Y. Yin. Mining Frequent Patterns without Candidate Generation. In Proc. of the ACM-SIGMOD 2000 Int. Conf. on Management of Data (SIGMOD’00), pp.1−12, Dallas, May 2000. [3] W. Li, J. Han and J. Pei. CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules. In Proc. of 2001 IEEE Int. Conf. on Data Mining (ICDM'01), pp , San Jose CA, Nov 2001. [4] J. Li, G. Dong, K. Ramamohanarao and L. Wong. DeEPs: A New Instance-Based Lazy Discovery and Classification System. Machine Learning. 54, pp , 2004. [5] Adriano Veloso, Wagner Meira Jr, and Mohammed J. Zaki. Lazy Association Classification. In Proc. of 2006 IEEE Int. Conf. on Data Mining (ICDM'06), pp , Hong Kong, Oct 2006. [6] Maria-Luiza Antonie, Osmar R. Zaiane, and Robert C. Holte. Learning to Use a Learned Model: A Two-Stage Approach to Classification. In Proc. of 2006 IEEE Int. Conf. on Data Mining (ICDM'06), pp , Hong Kong, Oct 2006. [7] Abdelaziz Berrado, George C. Runger. Using Metarules to Organize and Group Discovered Association Rules. Data Mining and Knowledge Discover. 14: , 2007. [8] F. Thabtah, P. Cowling, and Y. Peng. MCAR: Multi-class Classification based on Association Rule Approach. In Proceeding of the 3rd IEEE International Conference on Computer Systems and Applications. pp.1-7. Cairo, Egypt. Hebei University
22
[9] O. R. Zaiane and M. -L. Antonie
[9] O. R. Zaiane and M.-L. Antonie. On pruning and tuning rules for associative classifiers. In Proc. of Int'l Conf. on Knowledge-Based Intelligence Information & Engineering Systems (KES'05), pp , 2005. [10]Adriano Veloso, Wagner Meira Jr.: Rule Generation and Rule Selection Techniques for Cost-Sensitive Associative Classification. In SBBD pp , 2005. [11]J. Wang and G. Karypis. HARMONY: Efficiently Mining the Best Rules for Classification. In Proc. of 2006 SIAM Int. Conf. on Data Mining (SDM'05), California, USA, April 2005. [12]Bing Liu, Yiming Ma, C-K Wong, Classification Using Association Rules: Weaknesses and Enhancements. In Vipin Kumar, et al, (eds), Data mining for scientific applications, 2001 [13] X. Yin and J. Han. CPAR: Classification based on Predictive Association Rules. In Proc SIAM Int.Conf. on Data Mining (SDM'03), San Fransisco, CA, May 2003. [14] Frans Coenen and Paul Leng. The Effect of Threshold Values on Association Rule Based Classification Accuracy. Journal of Data and Knowledge Engineering, Vol. 60, Num. 2, pp , February 2007 [15] Frans Coenen, Paul Leng, and Lu Zhang. Threshold Tuning for Improved Classification Association Rule Mining. In Proc. of 6th Pacific Area Conference on Knowledge Discovery and Data Mining (PAKDD'05), pp , Taipei, May [16] Maria-Luiza Antonie and Osmar R. Zaiane, An Associative Classifier based on Positive and Negative Rules, In 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD-04), pp 64-69, Paris, France, June 2004 Hebei University
![[9] O. R. Zaiane and M. -L. Antonie](http://slidesplayer.com/slide/11228340/60/images/22/%5B9%5D+O.+R.+Zaiane+and+M.+-L.+Antonie.jpg "[9] O. R. Zaiane and M.-L. Antonie. On pruning and tuning rules for associative classifiers. In Proc. of Int l Conf. on Knowledge-Based Intelligence Information & Engineering Systems (KES 05), pp , [10]Adriano Veloso, Wagner Meira Jr.: Rule Generation and Rule Selection Techniques for Cost-Sensitive Associative Classification. In SBBD pp , [11]J. Wang and G. Karypis. HARMONY: Efficiently Mining the Best Rules for Classification. In Proc. of 2006 SIAM Int. Conf. on Data Mining (SDM 05), California, USA, April [12]Bing Liu, Yiming Ma, C-K Wong, Classification Using Association Rules: Weaknesses and Enhancements. In Vipin Kumar, et al, (eds), Data mining for scientific applications, [13] X. Yin and J. Han. CPAR: Classification based on Predictive Association Rules. In Proc SIAM Int.Conf. on Data Mining (SDM 03), San Fransisco, CA, May [14] Frans Coenen and Paul Leng. The Effect of Threshold Values on Association Rule Based Classification Accuracy. Journal of Data and Knowledge Engineering, Vol. 60, Num. 2, pp , February [15] Frans Coenen, Paul Leng, and Lu Zhang. Threshold Tuning for Improved Classification Association Rule Mining. In Proc. of 6th Pacific Area Conference on Knowledge Discovery and Data Mining (PAKDD 05), pp , Taipei, May [16] Maria-Luiza Antonie and Osmar R. Zaiane, An Associative Classifier based on Positive and Negative Rules, In 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD-04), pp 64-69, Paris, France, June Hebei University.")
23
[17] Yanbo J. Wang, Qin Xin and Frans Coenen
[17] Yanbo J. Wang, Qin Xin and Frans Coenen. A Novel Rule Ordering Approach in Classification Association Rule Mining. In Proc. MLDM'2007, pp [18] Frans Coenen and Paul Leng. An Evaluation of Approaches to Classification Rule Selection. In Proc. of 2004 IEEE Int. Conf. on Data Mining (ICDM'04), pp , 2004 [19] K. Wang, S. Zhou, and Y. He. Growing decision tree on support-less association rules. In Proc. Of 2000 Int. Conf. on Knowledge Discovery and Data Mining (KDD'00), Boston, MA, Aug [20]Frans Coenen and Paul Leng. Obtaining Best Parameter Values for Accurate Classification. In Proc. of 2005 IEEE Int. Conf. on Data Mining (ICDM'05), pp , 2005 [21] D. Meretakis and B. Wuthrich. Extending Naïve Bayes Classifiers Using Long Itemsets. In Proc Int. Conf. on Knowledge Discovery and Data Mining (KDD'99) , pages , San Diego, CA, Aug [22] Bing Liu, Yiming Ma, and Ching Kian Wong. Improving an Association Rule Based Classifier. In Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, Pages: 504 – 509, 2000 [23] Bavani Arunasalam and Sanjay Chawla. CCCS: A Top-down Associative Classifier for Imbalanced Class Distribution. In Proc. Of 2006 Int. Conf. on Knowledge Discovery and Data Mining (KDD'06), pp [24] Florian Verhein and Sanjay Chawla. Using Significant, Positively Associated and Relatively Class Correlated Rules for Associative Classification of Imbalanced Datasets, In Proc. of 2007 IEEE Int. Conf. on Data Mining (ICDM'07), 2007. Hebei University
![[17] Yanbo J. Wang, Qin Xin and Frans Coenen](http://slidesplayer.com/slide/11228340/60/images/23/%5B17%5D+Yanbo+J.+Wang%2C+Qin+Xin+and+Frans+Coenen.jpg "[17] Yanbo J. Wang, Qin Xin and Frans Coenen. A Novel Rule Ordering Approach in Classification Association Rule Mining. In Proc. MLDM 2007, pp [18] Frans Coenen and Paul Leng. An Evaluation of Approaches to Classification Rule Selection. In Proc. of 2004 IEEE Int. Conf. on Data Mining (ICDM 04), pp , [19] K. Wang, S. Zhou, and Y. He. Growing decision tree on support-less association rules. In Proc. Of 2000 Int. Conf. on Knowledge Discovery and Data Mining (KDD 00), Boston, MA, Aug [20]Frans Coenen and Paul Leng. Obtaining Best Parameter Values for Accurate Classification. In Proc. of 2005 IEEE Int. Conf. on Data Mining (ICDM 05), pp , [21] D. Meretakis and B. Wuthrich. Extending Naïve Bayes Classifiers Using Long Itemsets. In Proc Int. Conf. on Knowledge Discovery and Data Mining (KDD 99) , pages , San Diego, CA, Aug [22] Bing Liu, Yiming Ma, and Ching Kian Wong. Improving an Association Rule Based Classifier. In Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, Pages: 504 – 509, [23] Bavani Arunasalam and Sanjay Chawla. CCCS: A Top-down Associative Classifier for Imbalanced Class Distribution. In Proc. Of 2006 Int. Conf. on Knowledge Discovery and Data Mining (KDD 06), pp [24] Florian Verhein and Sanjay Chawla. Using Significant, Positively Associated and Relatively Class Correlated Rules for Associative Classification of Imbalanced Datasets, In Proc. of 2007 IEEE Int. Conf. on Data Mining (ICDM 07), Hebei University.")
24
问题？ 谢谢！ Hebei University
Similar presentations
