一种基于K近邻算法的图书馆读者分类方法

A Library Reader Classification Method Based on K-Nearest Neighbor Algorithm

在进行读者类型分类时,针对某一待分类读者,存在分类交叉的情况,从而难以分类。在对27位读者进行问卷调查获取读者样本数据的基础上,文章提出了采用K邻近(KNN)算法对读者进行分类的方法,详细阐述了算法分类过程,并进行实例结果分析。通过分析,该方法能够有效克服读者分类交叉的情况,分类过程易于操作,分类结果科学合理,为图书馆提高服务质量和读者满意度等工作提供参考依据。

When classifying reader types,there is a situation of cross classification for a certain reader to be classified,making it difficult to classify.On the basis of the sample data of 27 readers obtained by questionnaire survey,this paper proposes a method of using the K-nearest neighbor(KNN)algorithm to classify readers.The classification process of the algorithm is described in detail,and the result of an example is analyzed.Through the analysis,this method can effectively overcome the situation of cross classification,the classification process is easy to operate,and the classification results are scientific and reasonable,which can provide a reference for the library to improve the quality of service and reader satisfaction.