一种改进的KNN文本分类算法

当今大数据时代，文本数据占相当大的比重，作为有效管理和组织文本数据的方法，分类逐渐成为关注的热点。KNN是一种经典的分类算法，针对其分类速度和分类精度无法同时兼顾的不足，采用改进的K Medoids聚类算法裁剪对KNN分类贡献小的训练样本，从而减少KNN相似度的计算量，并定义代表度函数有差别地处理测试文本的K个最近邻文本，以提高KNN的分类精度。实验结果表明，改进后的方法在分类速度上和分类精度上均有明显地提高。

Improved KNN text classification algorithm

Text data accounts for a large proportion in the era of big data nowadays, text classification, as an effective method of managing and organizing text data, has attracted much attention. KNN is a classic classification algorithm, but its classification speed and accuracy cannot be considered synchronously. Aiming at this shortage, the improved K Medoids clustering algorithm is adopted to cut the training samples which make little contribution to the classification, to reduce the KNN similarity computation. The representativeness function is defined in order to treat K nearest neighbor samples of testing text differently, to enhance the accuracy of KNN. The results show that the improved method performs better than the traditional method both in speed and accuracy of classification.