Verhein F,Chawla S.Using Significant,Positively Associated and Relatively Class Correlated Rules for Associative Classification of Imbalanced Datasets // Proc of the 7th IEEE International Conference on Data Mining.Omaha,USA,2007: 679 - 684
[2]
Tsay Y J,Hsu T J,Yu J R.FIUT: A New Method for Mining Frequent Itemsets.Information Sciences: An International Journal,2009,179(11): 1724-1737
[3]
Burdick D,Calimlim M,Gehrke J.MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases // Proc of the 17th International Conference on Data Engineering.Heidelberg,Germany,2001: 443-452
[4]
Gouda K,Zaki M J.Efficiently Mining Maximal Frequent Itemsets // Proc of the IEEE International Conference on Data Mining.San Jose,USA,2001: 163-170
[5]
Wang Liming,Li Kun.Algorithm of Studying the Structure of Graphical Utility Based on Nearest-Biclusters Collaboration Filtering Technology.Chinese Journal of Computers,2010,33(12): 2291-2299 (in Chinese)(王黎明,李 琨.基于Nearest-Biclusters 协作过滤技术的效用图结构学习算法.计算机学报,2010,33(12): 2291-2299)
[6]
Luo Xiangfeng,Liang Guoning,Liu Shijun.Generating Associated Relation between Documents // Proc of the 10th IEEE International Conference on High Performance Computing and Communications.Dalian,China,2008: 831-836
[7]
Natarajan R,Shekar B.Understandability of Association Rules: A Heuristic Measure to Enhance Rule Quality // Guillet F J,Hamilton H J,eds.Quality Measures in Data Mining.New York,USA: Springer Uerlag,2007,43: 179-203
[8]
Hilderman R J,Peckham T.Statistical Methodologies for Mining Potentially Interesting Contrast Sets // Guillet F J,Hamilton H J,eds. Quality Measures in Data Mining.New York,USA: Springer Uerlag,2007,43: 153-177
[9]
Hilderman R J,Hamilton H J.Measuring the Interestingness of Discovered Knowledge: A Principled Approach.Intelligent Data Analysis,2003,7(4): 347-382
[10]
Hilderman R J,Hamilton H J.Evaluation of Interestingness Measures for Ranking Discovered Knowledge // Proc of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining.Shenzhen,China,2001: 247-259
[11]
Zhang Yulei,Zhao Liping.Quantitative Analysis of the Relationship of Bacteria Based on the Dinucleotide Frequency Profile.Chinese Journal of Microecology,2006,18(4): 261-263 (in Chinese)(张宇镭,赵立平.基于双核酸频率分布特征的细菌亲缘关系定量分析.中国微生态学杂志,2006,18(4): 261-263)
[12]
Gonzales C,Perny P,Queiroz S.Preference Aggregation with Graphical Utility Models // Proc of the 23rd AAAI Conference on Artificial Intelligence.Chicago,USA,2008: 1037-1042
[13]
Wang Liming,Li Kun.A Model of Negotiation Based on GAI Multi-Attribute Dependencies.Pattern Recognition and Artificial Intelligence,2008,21(5): 569-576 (in Chinese) (王黎明,李 琨.基于GAI多属性依赖的协商模型.模式识别与人工智能,2008,21(5): 569-576)
[14]
Wang Liming,Huang Houkuan,Chai Yumei.An Algorithm for Resource Negotiation Based on Progresses Reduction in Process of Calculating.Pattern Recognition and Artificial Intelligence,2005,18(3): 273-280 (in Chinese)(王黎明,黄厚宽,柴玉梅.推测计算中基于进程约简的资源协商算法.模式识别与人工智能,2005,18(3): 273-280)
[15]
Han Jiawei,Pei Jian,Yin Yiwen.Mining Frequent Patterns without Candidate Generation // Proc of the ACM SIGMOD International Conference on Management of Data.Dallas,USA,2000: 1-12
[16]
Tsay Y J,Chiang J Y.CBAR: An Efficient Method for Mining Association Rules.Knowledge Based Systems,2005,18(2/3): 99-105
[17]
Bashir S,Jan Z,Baig A R.Fast Algorithms for Mining Interesting Frequent Itemsets without Minimum Support [EB/OL].[2009-04-21].http://arxiv.org/ftp/arxiv/papers/0904/0904.3319.pdf
