K-Means优化算法的R语言实现

R Language Realization Based on K-Means Optimization Algorithm

随着数据量级的不断增大,大数据挖掘算法提出了新的需求与挑战。针对数据聚类的K-Means算法做了一些研究,阐述了K-Means在大数据分析中的经典算法与R语言的实现,分析其存在的弊端引入基于Map-Reduce函数的K-means算法;进一步地,通过迭代方法实现了基于Map-Reduce函数的K-means优化算法,并用R程序得以实现;最后给出了程序的测试与应用,验证算法的可行性。

With the continuously increasing data magnitude,big data mining raises new requirements and challenges.This paper did some research on K-Mean algorithm of data clustering,elaborates the classical algorithm and the realization of R language of KMeans in big data analysis,and analyzes the drawbacks of K-Means algorithm based on Map-Reduce function.Furthermore,the Kmeans optimization algorithm based on Map-Reduce function is realized through iterative method and it is implemented by R program.Finally,the test and application of the program are given,and the feasibility of the algorithm is verified.