• 理论与算法 •

### 犹豫模糊信息下的协相关度与聚类分析

1. 1. 安徽大学 数学科学学院，合肥 230601
2. 安徽大学 计算智能与信号处理教育部重点实验室，合肥 230039
3. 安徽师范大学 皖江学院 经济系，安徽 芜湖 241008
4. 安徽广播电视大学 教育科学学院，合肥 230022
• 出版日期:2018-05-01 发布日期:2018-05-07

### Co-correlation Degree under Hesitant Fuzzy Information and Clustering Analysis

WANG Feng1+, MAO Junjun1,2, ZU Xuan3, ZOU Bin4

1. 1. School of Mathematical Sciences, Anhui University, Hefei 230601, China
2. Key Lab of Intelligent Computing & Signal Processing of Ministry of Education, Anhui University, Hefei 230039, China
3. Department of Economics, Wanjiang College of Anhui Normal University, Wuhu, Anhui 241008, China
4. School of Educational Science, Anhui Radio and TV University, Hefei 230022, China
• Online:2018-05-01 Published:2018-05-07

Abstract: For the multi-attribute clustering problem that decision information is hesitant fuzzy set (HFS) and the attribute weights are completely unknown, this paper proposes a co-correlation degree based on hesitant fuzzy cross-entropy and clustering method. In order to distinguish different HFSs, this paper defines the cross-entropy between two hesitant fuzzy elements (HFEs), and verifies the validity and rationality in comparison with the results obtained by other distance formulas. Next, this paper derives the formula of attribute weights according to the maximizing deviation algorithm from the hesitant fuzzy cross-entropy. Then, this paper proposes the concept of co-correlation degree, proves its similar properties with the traditional correlation coefficient, and considers different attributes' weight at the same time, which is determined by the formula of attribute weights. Finally, this paper uses the weighted formula of co-correlation degree for clustering analysis under hesitant fuzzy information, and verifies the feasibility and effectiveness through the comparison analysis of clustering results of diverse literature.