[1]FENG Liuwei,CHANG Dongxia,DENG Yong,et al.A clustering evaluation index based on the nearest and furthest score[J].CAAI Transactions on Intelligent Systems,2017,12(1):67-74.[doi:10.11992/tis.201611007]
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A clustering evaluation index based on the nearest and furthest score

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 1 Page number: 67-74 Column: 学术论文—机器感知与模式识别 Public date: 2017-02-25
Title:
A clustering evaluation index based on the nearest and furthest score
Author(s):
FENG Liuwei1; 2;  CHANG Dongxia1; 2;  DENG Yong3;  ZHAO Yao1; 2
1. Institute of Information Science, Beijing Jiaotong University Beijing 100044, China;
2. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;
3. Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
Keywords:
the nearest neighbor consistency; the furthest neighbor difference; K-means clustering algorithm; scoring mechanism; evaluation index; hierarchical clustering
CLC:
TP391
DOI:
10.11992/tis.201611007
Abstract:
The clustering algorithm is one of the widely-used methods in data analysis. However, the number of clusters is essential to determine the performance of the clustering algorithm. At present, the number of clusters usually need to be specified in advance. In most cases, it is difficult to obtain the valid cluster number according to a priori knowledge of the dataset. To obtain the number of clusters automatically, a Nearest and Furthest Score (NFS) index was proposed based on the principles of the nearest neighbor consistency and the furthest neighbor difference. Moreover, an Automatic Clustering NFS (ACNFS) algorithm was also proposed, which can determine the number of clusters automatically. The experimental results prove the index is reasonable and practicable to determine the cluster number.
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