• 人工智能与模式识别 •

### 有效距离在聚类算法中的应用

1. 1. 南京航空航天大学 计算机科学与技术学院，南京 211106
2. 泰山学院 信息科学技术学院，山东 泰安 271021
• 出版日期:2017-03-01 发布日期:2017-03-09

### Application of Effective Distance in Clustering Algorithms

GUANG Junye1, LIU Mingxia1,2, ZHANG Daoqiang1+

1. 1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing   211106, China
2. College of Information Science and Technology, Taishan University, Taian, Shandong 271021, China
• Online:2017-03-01 Published:2017-03-09

Abstract:  Distance metric learning is a key step in clustering analysis, which is an important sub-domain of data mining. Euclidean distance metric is a quite commonly used local distance metric in clustering algorithms, which only focuses on the distance between two samples. This paper proposes a new global distance metric method, named as the effective distance metric. In the new method, the similarity between two samples is evaluated by using not only the distance between these two samples, but also distances between one specific sample and all the other related ones. Sparse reconstruction coefficients are employed to reflect such global relationship among samples. Then, this paper develops three effective distance-based clustering algorithms, including EK-means, EK-medoids and EFCM, by applying the effective distance to three classical clustering algorithms, i.e., K-means, K-medoids and FCM (fuzzy C-means), respectively. The experimental results on UCI benchmark datasets demonstrate the efficacy of the proposed methods.