Self-updating Clustering Algorithm for Interval-valued Data

被引：0

作者：

Hung, Wen-Liang ^{[1
]}

Yang, Jenn-Hwai ^{[2
]}

Shen, Kuan-Fu ^{[3
]}

机构：

[1] Natl Hsinchu Univ Educ, Dept Appl Math, Hsinchu, Taiwan

[2] Acad Sinica, Inst Biomed Sci, Taipei, Taiwan

[3] Chien Hsin Univ Sci & Technol, Dept Finance, Taoyuan, Taiwan

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) | 2016年

关键词：

DISTANCES;

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：

This paper proposes a robust automatic clustering algorithm based on the Hausdorff distance, called the self-updating clustering algorithm, for interval-valued data. This algorithm can simulate the self-clustering process. At the end of the clustering process, interval-valued data belonging to the same cluster converge to the same position, which represents the cluster's center. The numerical results show the effectiveness of the proposed algorithm using the overall error rate of classification (OERC) and the corrected rand (CR) index as criteria. An example of exoplanet data is also presented.

引用

页码：1494 / 1500

页数：7

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