[1]李燕,张玉红,胡学钢. 基于C4.5和NB混合模型的数据流分类算法[J].计算机科学,2010,37(12):138-142.[2]WIDMER G, KUBAT M. Learning in the presence of concept drift and hidden contexts [J]. Machine Learning,1996,23(1):69-101.[3]王黎明,周驰.自适应概念漂移的在线集成分类器[J].计算机工程,2011,37(5):74-76.[4]TSYMBAL A, PECHENIZKIY M, CUNNINGHAM P, et al. Dynamic integration of classifiers for handling concept drift [J]. Information Fusion, 2008,9(1):56-68.[5]HANSEN L K, SALAMON P. Neutral network ensemble [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(10):993-1001.[6]WANG H, FAN W, YU P. et al. Mining concept drifting data streams using ensemble classifiers [C]// KDD'03: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2003:226-235.[7]STREET W, KIM Y. A Streaming Ensemble Algorithm (SEA) for large-scale classification [C]// KDD'01: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2001:77-382.[8]胡学刚,潘春香.基于实例加权方法的概念漂移问题研究[J].计算机工程与应用, 2008, 44(21):188-190.[9]欧阳震诤,罗建书,胡东敏,等.一种不平衡数据流集成分类模型[J].电子学报,2010,38(1):184-189.[10]张健沛,杨显飞,杨静.面向高速数据流的偏倚抽样集合分类器[J].北京邮电大学学报,2010,33(4):44-48.[11]JEREMY Z K, MARCUS A M. Dynamic weighted majority: An ensemble method for drifting concepts [J]. Journal of Machine Research, 2007,8(12):2755-2790.[12]文益民,王耀南,张莹.基于可信多数投票的快速概念漂移检测[J].湖南大学学报,2010,37(6):36-40.[13]关菁华,刘大有.一种挖掘概念漂移数据流的选择性集成算法[J].计算机科学,2010,37(1):204-207.[14]COVER T M, HART P E. Nearest neighbor pattern classification [J]. IEEE Transactions on Information Theory,1967,13(1):21-27.[15]YANG Q, WU X. 10 Challenging problems in data mining research [J]. Journary of Information Technology and Decision Making,2006,5(4):597-604.[16]鲁婷,王浩,姚宏亮.一种基于中心文档的KNN中文文本分类算法[J].计算机工程与应用,2011,47(2):127-130.[17]AGGARWAL C C, PROCOPIUC C, WOLF J L, et al. Fast algorithm for projected clustering [C]// SIGMOD'99: Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data. New York: ACM Press,1999: 61-71.[18]MOISE G, SANDER J, ESTER M. Robust projected clustering [J]. Knowledge Information System, 2008, 14(3)273-398.[19]HUANG J Z, NG M K, RONG H, et al. Automated variable weighting in k-means type clustering [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(5):657-668.[20]盛骤,谢式千,潘承毅.概率论与数理统计[M].北京:高等教育出版社,2006:241-243.[21]HULTEN G, SPENCER L, DOMINGOS P. Mining time-changing data streams [C]// KDD'01: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2001:97-106. |