华中师范大学学报（自然科学版）2017，Vol.51Issue(6)：754-759,6.DOI:10.19603/j.cnki.1000-1190.2017.06.005
基于模糊C-Means的改进型KNN分类算法
Improved KNN classification algorithm based on Fuzzy C-Means
摘要
Abstract
KNN algorithm is a classification algorithm that is simple and easy to implement,but when the training set is rather big and features are more,its efficiency is low with which takes more time.To solve this problem,an improved KNN classification algorithm was proposed based on Fuzzy C-Means.The improved algorithm introduces the theory of Fuzzy C-Means based on the traditional KNN classification algorithm.Through processing the sample data clustering,the formation of sub clusters substitutes all the sample set of the sub cluster,which helps reduce the number of training set.Thereby the workload of the KNN classification process is reduced,with the classification efficiency improved and the KNN algorithm better applied in data mining.The theoretical analysis and experimental results show that this method is able to significantly improve the efficiency and accuracy of the algorithm when dealing with large data,meeting the demand of processing data.关键词
模糊C-Means/聚类/KNN分类Key words
Fuzzy C-Means/clustering/KNN classification分类
信息技术与安全科学引用本文复制引用
朱付保,谢利杰,汤萌萌,朱颢东..基于模糊C-Means的改进型KNN分类算法[J].华中师范大学学报（自然科学版）,2017,51(6):754-759,6.基金项目
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) （13A52036）
河南省高等学校青年骨干教师资助计划项目(2014GGJS-084) （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) （XGGJS02）
郑州轻工业学院博士科研基金资助项目(2010BSJJ038). （2010BSJJ038）
