基于Spark的压缩近邻算法

Spark Based Condensed Nearest Neighbor Algorithm

K-近邻(K-Nearest Neighbors,K-NN)是一种懒惰学习算法,用K-NN对数据分类时,不需要训练分类模型。K-NN算法的优点是思想简单、易于实现;缺点是计算量大,原因是在对测试样例进行分类时,其需要计算测试样例与训练集中每一个训练样例之间的距离。压缩近邻算法(Condensed Nearest Neighbors,CNN)可以克服K-NN算法的不足。但是,在面对大数据集时,由于自身的迭代计算特性,CNN的运算效率会变得非常低。针对这一问题,提出一种名为Spark CNN的压缩近邻算法。在大数据环境下,与基于MapReduce的CNN算法相比,Spark CNN的效率大幅提高,在5个大数据集上的实验证明了这一结论。

K-nearest neighbors（K-NN）is a lazy learning algorithm.It is unnecessary to train classification models,when one uses K-NN for data classification.K-NN algorithm is simple and easy to implement.The disadvantages of KNN is that it requires large number of computations,which is introduced by calculating distances between testing instance and every training instance.Condensed nearest neighbors（CNN）can overcome the drawback of K-NN mentioned above.However,CNN is an iterative algorithm,when it is applied in big data scenario,its efficiency becomes very low.In order to deal with this problem,this paper proposed an algorithm named Spark CNN.In big data circumstances,Spark CNN can significantly improve the efficiency of CNN.This paper experimentally compared the Spark CNN with MapReduce CNN on 5 big data sets,the experimental results show that the Spark CNN is very effective.