李丹

个人信息Personal Information

副教授

硕士生导师

性别:女

毕业院校:大连理工大学

学位:博士

所在单位:控制科学与工程学院

办公地点:大连理工大学创新园大厦A716

电子邮箱:ldan@dlut.edu.cn

扫描关注

论文成果

当前位置: 中文主页 >> 科学研究 >> 论文成果

A fuzzy c-means clustering algorithm based on nearest-neighbor intervals for incomplete data

点击次数:

论文类型:期刊论文

发表时间:2010-10-01

发表刊物:EXPERT SYSTEMS WITH APPLICATIONS

收录刊物:SCIE、EI、Scopus

卷号:37

期号:10

页面范围:6942-6947

ISSN号:0957-4174

关键字:Clustering; Fuzzy c-means; Incomplete data; Nearest-neighbor intervals

摘要:Partially missing data sets are a prevailing problem in clustering analysis. In this paper, missing attributes are represented as intervals, and a novel fuzzy c-means algorithm for incomplete data based on nearest-neighbor intervals is proposed. The algorithm estimates the nearest-neighbor interval representation of missing attributes by using the attribute distribution information of the data sets sufficiently, which can enhances the robustness of missing attribute imputation compared with other numerical imputation methods. Also, the convex hyper-polyhedrons formed by interval prototypes can present the uncertainty of missing attributes, and simultaneously reflect the shape of the clusters to some degree, which is helpful in enhancing the robustness of clustering analysis. Comparisons and analysis of the experimental results for several UCI data sets demonstrate the capability of the proposed algorithm. (C) 2010 Elsevier Ltd. All rights reserved.