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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (12): 84-91    DOI: 10.11925/infotech.2096-3467.2017.0724
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Uncertain Data Clustering Algorithm Based on Local Density
Luo Yanfu1, Qian Xiaodong2()
1School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
2School of Economics and Management, Lanzhou Jiaotong University, Lanzhou 730070, China
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[Objective] This paper proposes a new algorithm to cluster uncertain data, aiming to reduce the shortcomings inherited from the classic ones. [Methods] First, we modified the measurement of uncertain distance and compared the probability differences between two existing uncertain objects. Then, we defined the cluster centers and proposed a new algorithm to group the data into the related clusters based on the concepts of maximum supporting points and density chain regions. [Results] We used two data sets from the UCI machine learning library to examine the proposed algorithm. We found that the F values of the two data sets increased by 13.23% and 23.44% compared to traditional algorithm (UK-Means and FDBSCAN). It took the algorithm longer time to calculate the distance matrix. Therefore, the overall clustering time was only slightly shorter than the traditional algorithm. [Limitations] There was no appropriate method to define the parameter for the proposed algorithm, and the clustering time was complex. [Conclusions] The proposed algorithm could quickly determine the clustering centers and complete the clustering tasks. The value of t (the only parameter) poses much influence to the clustering results.

Key wordsUncertain Data      Cut-off Distance      Local Density      Density Chain Region     
Received: 24 July 2017      Published: 29 December 2017
ZTFLH:  TP393  

Cite this article:

Luo Yanfu,Qian Xiaodong. Uncertain Data Clustering Algorithm Based on Local Density. Data Analysis and Knowledge Discovery, 2017, 1(12): 84-91.

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簇号 聚类中心(标号) 簇内点的个数 离群点个数
1 28 P: 50; N: 0 2
2 92 P: 50; N: 3
3 148 P: 28; N: 17
簇号 聚类中心(标号) 簇内点的个数 离群点个数
1 687 P: 4467; N: 0 0
2 829 P: 16635; N: 42
3 29878 P: 44473; N: 1940
算法 F值 运行时间(s)
Iris Connect-4 Iris Connect-4
UK-Means 0.8865 0.8017 0.0261 41.2505
FDBSCAN 0.7983 0.7430 0.0442 47.3241
本文算法 0.9854 0.9085 0.0250 37.9223
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