相似文献/References:
[1]何中胜 庄燕滨.基于Apriori&Fp—growth的频繁项集发现算法[J].计算机技术与发展,2008,(07):45.
HE Zhong-sheng,ZHUANG Yan-bin.Algorithm of Mining Frequent Itemset Based on Apriori and Fp - growth[J].,2008,(10):45.
[2]李志云 周国祥.一种基于MFP树的快速关联规则挖掘算法[J].计算机技术与发展,2007,(06):94.
LI Zhi-yun,ZHOU Guo-xiang.A Fast Association Rule Mining Algorithm Based on MFP Tree[J].,2007,(10):94.
[3]楼巍 刘捷 严利民.协同进化算法在关联规则挖掘中的应用[J].计算机技术与发展,2012,(11):13.
LOU Wei,LIU Jie,YAN Li-min.Applied Research on Association Rules Mining with Co-evolution Algorithm[J].,2012,(10):13.
[4]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(10):1.
[5]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(10):5.
[6]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(10):13.
[7]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(10):21.
[8]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(10):25.
[9]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(10):29.
[10]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(10):34.
[11]赵阳,吴廖丹. 一种自底向上的最大频繁项集挖掘方法[J].计算机技术与发展,2017,27(08):57.
ZHAO Yang,WU Liao-dan. A Bottom-up Method for Mining Maximum Frequent Itemsets[J].,2017,27(10):57.