改进蚁群算法MMAS在分类规则挖掘中的研究 |
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引用本文: | 陈宝钢,;唐飞,;蔡铁,;陆芸婷,;刘寿强.改进蚁群算法MMAS在分类规则挖掘中的研究[J].计算机技术与发展,2014(6):179-183. |
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作者姓名: | 陈宝钢 ;唐飞 ;蔡铁 ;陆芸婷 ;刘寿强 |
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作者单位: | [1]河南农业大学信息与管理科学学院,河南郑州450046; [2]深圳信息职业技术学院,广东深圳518029; [3]华南师范大学物理与电信工程学院,广东广州510006 |
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基金项目: | 广东省自然科学基金项目(S2011010003890,S2013010012669,S2011010006115); 深圳市科技计划项目(JC201105190829A); 河南省科技攻关计划项目(11210221019) |
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摘 要: | 为深入研究和评估蚁群算法在分类规则挖掘应用中具有的特点和作用,针对目前基本蚁群算法在数据挖掘方面所存在的不足,引入了改进的蚁群算法模型最大最小蚂蚁系统(MMAS)。并根据分类算法比较原则,通过实验分析对分类规则挖掘算法进行比较。根据使用不同数据集实验结果的对比分析,从仿真的精确度、速度等方面展示和证实了基于改进的蚁群算法模型MMAS的数据分类规则挖掘工具AntMiner+在分类规则挖掘中体现出的特点和优势。
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关 键 词: | 数据挖掘 分类规则 蚁群算法 最大最小蚂蚁系统 AntMiner+ |
Research on Improved Ant Colony Algorithm MMAS in Classification Rule Mining |
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Affiliation: | CHEN Bao-gang ,TANG Fei, CAI Tie ,LU Yun-ting ,LIU Shou-qiang (1. College of Information and Management Science, Henan Agriculture University ,Zhengzhou 450046, China; 2. Shenzhen Institute of Information Technology, Shenzhen 518029, China; 3. School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China) |
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Abstract: | In order to study and evaluate the features and functions of ant colony algorithm in classification rule mining applications in-depth,aiming at the deficiencies of the basic ant colony algorithm,introduce improved ant colony algorithms,the Max-Min Ant System( MMAS).And according to the comparison principle of the classification rule mining algorithm,make a comparison for classification rule mining algorithms through the experimental analysis.The results show that the AntMiner + based on MMAS model has great advantages in the classification rule mining from simulation accuracy and speed. |
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Keywords: | data mining classification rule ant colony algorithm Max-Min Ant System(MMAS) AntMiner + |
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