聚类算法综述  

Review of clustering algorithms

作　　者：章永来 周耀鉴 ZHANG Yonglai;ZHOU Yaojian(Software School,North University of China,Taiyuan Shanxi 030051,China)

机构地区：[1]中北大学软件学院

出　　处：《计算机应用》2019年第7期1869-1882,共14页journal of Computer Applications

基　　金：国家自然科学基金资助项目(6160051296)~~

摘　　要：大数据时代,聚类这种无监督学习算法的地位尤为突出。近年来,对聚类算法的研究取得了长足的进步。首先,总结了聚类分析的全过程、相似性度量、聚类算法的新分类及其结果的评价等内容,将聚类算法重新划分为大数据聚类与小数据聚类两个大类,并特别对大数据聚类作了较为系统的分析与总结。此外,概述并分析了各类聚类算法的研究进展及其应用概况,并结合研究课题讨论了算法的发展趋势。Clustering is very important as an unsupervised learning algorithm in the age of big data. Recently,considerable progress has been made in the analysis of clustering algorithm. Firstly,the whole process of clustering,similarity measurement,new classification of clustering algorithms and evaluation on their results were summarized. Clustering algorithms were divided into two categories: big data clustering and small data clustering,and the systematic analysis and summary of big data clustering were carried out particularly. Moreover,the research progress and application of various clustering algorithms were summarized and analyzed,and the development trend of clustering algorithms was discussed in combination with the research topics.

关 键 词：聚类 相似性度量 大数据聚类 小数据聚类 聚类评价 

分 类 号：TP301[自动化与计算机技术—计算机系统结构] TP18[自动化与计算机技术—计算机科学与技术]