An Improved FCM Clustering Method for Interval Data

被引：0
|
作者：
Gu, Shen-Ming [1 ]
Zhao, Jian-Wen [1 ,2 ]
He, Ling [3 ]
机构：
[1] Zhejiang Ocean Univ, Sch Math Phys & Informat Sci, Zhoushan 316000, Zhejiang, Peoples R China
[2] Zhejiang Ocean Univ, Xiaoshan Sch, Hangzhou 311200, Peoples R China
[3] Zhejiang Ocean Univ, Sch Foreign Languages, Zhengzhou 316000, Peoples R China
来源：
ROUGH SET AND KNOWLEDGE TECHNOLOGY (RSKT) | 2010年 / 6401卷
基金：
中国国家自然科学基金;
关键词：
Clustering; combined distances; FCM; interval data; SEGMENTATION; DISTANCE;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In fuzzy c-means (FCM) clustering algorithm, each data point belongs to a cluster with a degree specified by a membership grade. Furthermore, FCM partitions a collection of vectors in c fuzzy groups and finds a cluster center in each group so that the objective function is minimized. This paper introduces a clustering method for objects described by interval data. It extends the FCM clustering algorithm by using combined distances. Moreover, simulated experiments with interval data sets have been performed in order to show the usefulness of this method.
引用
收藏
页码：545 / 550
页数：6
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