计算机应用与软件2011，Vol.28Issue(5)：112-113,125,3.
基于K-均值聚类的小样本集KNN分类算法
KNN CLASSIFICATION ALGORITHM FOR SMALL SAMPLE SETS BASED ON K-MEANS CLUSTERING
摘要
Abstract
When KNN and its improved algorithms are performing classification, it always influences the final classification accuracy because of either too dense or too few the samples or too large the density differences among various kinds of samples. The paper proposes a small sample set KNN classification algorithm based on clustering technology. A new sample set is generated through clustering and editing which contains various kinds of samples with close densities. That new sample set is used to classify and label data objects whose classification and label numbers are unknown. Tests by standard data sets reveal that the algorithm can improve KNN classification accuracy and obtain satisfactory results.关键词
K-均值聚类/K最近邻/小样本Key words
K-means clustering/ K-nearest-neighbor/ Small sample set引用本文复制引用
刘应东,牛惠民..基于K-均值聚类的小样本集KNN分类算法[J].计算机应用与软件,2011,28(5):112-113,125,3.基金项目
甘肃省自然科学研究基金规划项目(1010RJZA069). （1010RJZA069）
