An improved clustering method for uncertain system

Jingjing Ye (), Keping Li and Jing Li ()
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Jingjing Ye: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, P. R. China
Keping Li: #x2020;State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, P. R. China
Jing Li: #x2020;State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, P. R. China

International Journal of Modern Physics C (IJMPC), 2019, vol. 30, issue 09, 1-13

Abstract: Uncertain system clustering is an important issue which is the base for mining real-world data in many fields. In this paper, we investigate the clustering problem of uncertain system, and propose an improved clustering algorithm. Here, our improved algorithm considers not only distance but also spatial direction in vector space of data points. The aim is to improve the accuracy of uncertain system clustering, especially when there exists overlap among border region of groups in vector space. Experiment results show that for uncertain physics systems, the improved algorithm can well increase the accuracy of uncertain system clustering compared with the traditional clustering method which is based on distance similarity. Maximum value of accuracy and f-measure are increased by 21.1% and 13.3%, respectively. Moreover, the proposed algorithm has high robustness for noise.

Keywords: Uncertain system; clustering; similarity (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0129183119500761

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