Clustering by Fast Search and Find of Density Peaks with Data Field

WANG Shuliang; WANG Dakui; LI Caoyuan; LI Yan; DING Gangyi

doi:10.1049/cje.2016.05.001

Volume 25 Issue 3

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WANG Shuliang, WANG Dakui, LI Caoyuan, et al., “Clustering by Fast Search and Find of Density Peaks with Data Field,” Chinese Journal of Electronics, vol. 25, no. 3, pp. 397-402, 2016, doi: 10.1049/cje.2016.05.001

Citation:

WANG Shuliang, WANG Dakui, LI Caoyuan, et al., “Clustering by Fast Search and Find of Density Peaks with Data Field,” Chinese Journal of Electronics, vol. 25, no. 3, pp. 397-402, 2016, doi: 10.1049/cje.2016.05.001

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Clustering by Fast Search and Find of Density Peaks with Data Field

doi: 10.1049/cje.2016.05.001

1.
International School of Software, Wuhan University, Wuhan 430079, China;
2.
School of Software, Beijing Institute of Technology, Beijing 100081, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61173061, No.61472039, No.71201120) and the Doctoral Fund of Higher Education (No.20121101110036).

More Information

Corresponding author: WANG Dakui is a Ph.D. candidate inWuhan Universiry in China. His research interests include data field and data mining. (Email: dkwang2013@whu.edu.cn)
Received Date: 2015-04-08
Rev Recd Date: 2015-06-03
Publish Date: 2016-05-10

Abstract

Abstract

A clustering algorithm named "Clustering by fast search and find of density peaks" is for finding the centers of clusters quickly. Its accuracy excessively depended on the threshold, and no efficient way was given to select its suitable value, i.e., the value was suggested be estimated on the basis of empirical experience. A new way is proposed to automatically extract the optimal value of threshold by using the potential entropy of data field from the original dataset. For any dataset to be clustered, the threshold can be calculated from the dataset objectively instead of empirical estimation. The results of comparative experiments have shown the algorithm with the threshold from data field can get better clustering results than with the threshold from empirical experience.
- Data field,
- Potential entropy,
- Big data clustering,
- Optimal threshold value,
- Automatic extraction

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References(25)

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