K均值优化算法综述

k-means算法源于信号处理中的一种向量量化方法,现在则更多地作为一种聚类分析方法流行于数据挖掘领域。在数据挖掘技术中常常使用聚类方法,而k-means算法作为最典型、最常见、实用度最广的一种聚类算法,具有简单易操作等优点。但此算法需要人工设定聚类中心的数量,初始聚类中心,容易陷入局部最优,使得算法的时间复杂度变得较大,得到的聚类结果易受到k值与设定的初始聚类中心的影响,针对这些问题,本文介绍了k-means算法的改进方法,分析其优缺点并提出了优化算法的下一步研究方向。

A Survey on Advanced K-means Algorithm

K-means algorithm originated from a vector quantization method in signal processing and is now more popular in the field of data mining as a clustering analysis method.Clustering method is often used in data mining technology,and k-means algorithm,as the most typical,the most common and the most practical clustering algorithm,has the advantages of simple and easy operation.But this algorithm need to manually set the number of cluster centers,the initial clustering center,easy to fall into local optimum,makes the time complexity of the algorithm is larger,the clustering results are susceptible to k value and setting of the influence of the initial clustering center,to solve these problems,this paper introduces the improvement methods of k-means algorithm,analyzes the advantages and disadvantages and puts forward the optimization algorithm of the next research direction.