A Novel Algorithm DBCAPSIC for Clustering Non-Numeric Data

摘要：

　　Data mining techniques are playing an important role in the analysis of mass network information and big data nowadays.The cluster analysis,as a main kind of method in data mining,draws great interest from researchers of various fields who proposed many algorithms such as k-means algorithm and its variants,density-based algorithm and its variants.However,these algorithms all have their own problems.This paper focuses on some of the problems and proposes a novel algorithm DBCAPSIC.The algorithm overcomes the k-means algorithms sensitivity to initial conditions and avoids common density-based algorithms clustering failure in some cases.Also,the algorithm has the linear time complexity of O(n),compared to the quadratic time complexity of common density-based clustering algorithms.

关键词： k-means density DBCAPSIC sensitivity clustering failure

作者: Jinkun Geng Daren Ye Ping Luo

作者单位: School of Software,Beihang University,Beijing 100191,China Key Laboratory for Information System Security, Ministry of Education;Tsinghua National Laboratory f

会议类型: 国际会议

会议名称: 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference(IAEAC 2015)(2015 IEEE先进信息技术,电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 295-304

在线出版日期: 2015-12-19（万方平台首次上网日期，不代表论文的发表时间）