数据挖掘常用聚类算法研究  

Data Mining Clustering Algorithm

作　　者：赵学武[1] 刘向娇[1] 尹孟洋 ZHAO Xue-wu, LIU Xiang-jiao, YIN Meng-yang （Software Collage in Nanyang Normal University, Nanyang 473061, China）

机构地区：[1]南阳师范学院软件学院,河南南阳473061

出　　处：《电脑知识与技术》2014年第6期3710-3712,3731,共4页Computer Knowledge and Technology

基　　金：河南省基础与前沿技术研究计划项目（132300410439,142300410183,142300410182,132300410433）;南阳师范学院校级项目（QN2010010.QN2013040）

摘　　要：信息社会的发展，使数据量以前所未有的速度在增长，因此从海量数据中获取有用的知识和信息就变得越来越重要。数据挖掘是一种综合多领域知识而形成的数据分析技术，能够从大量数据中获取有价值的知识并为决策提供支持。聚类分析算法是数据挖掘中的一个核心内容，也是目前研究的一个热点。该文首先讲述了基于划分的聚类算法、基于分层的聚类算法、基于密度的聚类算法和基于网格的聚类算法等常用的聚类分析算法，并分析了其特点；然后通过举例详细描述了最近邻聚类算法的操作过程。聚类算法的总结，对聚类的研究和发展具有积极意义。The development of the information society make the amount of data growing at an unprecedented rate, and so to ob-tain useful knowledge from huge amounts of data and information becomes more and more important. Data mining is a data anal-ysis technique formed by integrating multi-domain knowledge, which can acquire valuable knowledge from large amounts of da-ta and provide support for decision. Clustering analysis algorithm in data mining is a core content, which is also a hotspot in the research of the current. This article first describes commonly used clustering algorithms that include the clustering algorithm based on classification, the clustering algorithm based on hierarchies and the clustering algorithm based on density and the clustering al-gorithm based grid, and then analyzes their characteristics. The operation process of nearest neighbor clustering algorithm is illus-trated in detail by an example. The summary of the clustering algorithms has positive significance for the research and develop-ment of clustering.

关 键 词：数据挖掘 聚类 聚类算法 簇 核密度 

分 类 号：TP18[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程]