基于模糊C-Means的改进型KNN分类算法
发布时间：2018-12-13 01:34
【摘要】：KNN算法是一种思想简单且容易实现的分类算法,但在训练集较大以及特征属性较多时候,其效率低、时间开销大.针对这一问题,论文提出了基于模糊C-means的改进型KNN分类算法,该算法在传统的KNN分类算法基础上引入了模糊C-means理论,通过对样本数据进行聚类处理,用形成的子簇代替该子簇所有的样本集,以减少训练集的数量,从而减少KNN分类过程的工作量、提高分类效率,使KNN算法更好地应用于数据挖掘.通过理论分析和实验结果表明,论文所提算法在面对较大数据时能有效提高算法的效率和精确性,满足处理数据的需求.
[Abstract]:KNN algorithm is a simple and easy to implement classification algorithm, but when the training set is large and the feature attributes are more, its efficiency is low and the time cost is large. In order to solve this problem, an improved KNN classification algorithm based on fuzzy C-means is proposed. The fuzzy C-means theory is introduced based on the traditional KNN classification algorithm. In order to reduce the number of training sets, reduce the workload of the KNN classification process, improve the classification efficiency and make the KNN algorithm better applied to data mining. The theoretical analysis and experimental results show that the proposed algorithm can effectively improve the efficiency and accuracy of the algorithm in the face of large data and meet the needs of data processing.
【作者单位】： 郑州轻工业学院计算机与通信工程学院;
【基金】：河南省科技攻关项目(162102210146;162102310579) 河南省教育厅科学技术研究重点项目(13A52036) 河南省高等学校青年骨干教师资助计划项目(2014GGJS-084) 郑州轻工业学院校级青年骨干教师培养对象资助计划项目(XGGJS02);郑州轻工业学院博士科研基金资助项目(2010BSJJ038)
【分类号】：TP311.13
[Abstract]:KNN algorithm is a simple and easy to implement classification algorithm, but when the training set is large and the feature attributes are more, its efficiency is low and the time cost is large. In order to solve this problem, an improved KNN classification algorithm based on fuzzy C-means is proposed. The fuzzy C-means theory is introduced based on the traditional KNN classification algorithm. In order to reduce the number of training sets, reduce the workload of the KNN classification process, improve the classification efficiency and make the KNN algorithm better applied to data mining. The theoretical analysis and experimental results show that the proposed algorithm can effectively improve the efficiency and accuracy of the algorithm in the face of large data and meet the needs of data processing.
【作者单位】： 郑州轻工业学院计算机与通信工程学院;
【基金】：河南省科技攻关项目(162102210146;162102310579) 河南省教育厅科学技术研究重点项目(13A52036) 河南省高等学校青年骨干教师资助计划项目(2014GGJS-084) 郑州轻工业学院校级青年骨干教师培养对象资助计划项目(XGGJS02);郑州轻工业学院博士科研基金资助项目(2010BSJJ038)
【分类号】：TP311.13
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