计算机学报模板

[1] 王德兴, 胡学钢,刘晓平 基于概念格的属性归纳算法研究 计算机学报(已录用，待发表), [2] 王德兴,胡学钢, 刘晓平 基于量化扩展概念格的属性归纳算法研究 模式识别与人工智能, 6,20(6):843-848[3] 王德兴,胡学钢, 刘晓平 基于量化扩展概念格的属性归纳算法与关联规则挖掘研究 系统工程学报(已录用,待发表),[4] 王德兴,胡学钢, 刘晓平 一种新颖的基于量化概念格的属性归纳算法 西安交通大学学报, 2 41(2):176-[5] 刘晓平,王德兴, 胡学钢 基于概念格模板优化的关联规则挖掘 工程图学学报, 12, 27(6):26-30[6] 王德兴,胡学钢,刘晓平, 王浩 改进购物篮分析的关联规则挖掘算法研究, 重庆大学学报, 2006, 29(6):105-[7] 王德兴,胡学钢,刘晓平,王浩 基于概念格和Apriori算法的关联规则挖掘分析, 合肥工业大学学报, 2006, 29(6):699-[8] Dexing Wang, Xuegang Hu, Xiaoping Liu, Hao WAssociation Rules Mining on Concept Lattice Using Domain K In: IEEE Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, A18-21, 2005, p2151-2154 (EI、ISTP索引)[9] Dexing Wang, Xuegang Hu, Xiaoping Liu, Hao Wang, Research on Model of Association Rules Mining with Added-newly Measure C In: IEEE Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, A 24-29, 2004, p1187- (EI、ISTP索引)[10]王德兴, 胡学钢, 王浩 基于量化概念格的关联规则挖掘 合肥工业大学学报(自然科学),10,25(5):678-682[11]王德兴, 胡学钢, 王浩 基于概念格的兴趣度加权的关联规则挖掘模型研究 南京大学学报（自然科学版）,计算机专辑,9,p89-95[12]Dexing Wang, Xuegang Hu, Hao W The Research on Model of Mining Association Rules Based on Quantitative Extended Concept Lattice, In: IEEE Proceedings of the First International Conference on Machine Learning and Cybernetics, N, 2002, Beijing, p134- (EI、ISTP索引)[13] 胡学刚,徐勇,王德兴,张晶 基于多剪枝格的频繁项集表示与挖掘 合肥工业大学学报 4,30(4) :432-435[14] Xuegang Hu, Xiang Wei, Dexing Wang, Peipei L A Parallel Algorithm to Construct Concept L Fuzzy Systems And Knowledge Discovery (Fskd'07),Jingsheng Lei, Jian Yu(eds), The IEEE Computer Society Press,S Vol 2, p119-123 (EI索引)[15]Xuegang Hu, Dexing Wang, Xiaoping Liu, Hao W The Analysis on Model of Association Rules Mining Based on Concept Lattice and Aprori Algorithm, In: IEEE Proceedings of the 3rd International Conference on Machine Learning and Cybernetics, Shanghai, A24-29, p1620- (EI、ISTP索引)[16]Xuegang Hu, Xiefei Hu, Dexing Wang, Dongyan, Zhang, Chunling H A Classification Algorithm Based on Multi-Relation Domain K In: IEEE Proceedings of the 4th International Conference on Machine Learning and Cybernetics, Guangzhou, A18-21, p2067-2072