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

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...

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Bibliographic Details
Published in:Chinese Journal of Electronics 2016-05, Vol.25 (3), p.397-402
Main Authors: Wang, Shuliang, Wang, Dakui, Li, Caoyuan, Li, Yan, Ding, Gangyi
Format: Article
Language:English
Subjects:
Automatic extraction
Big data clustering
clustering algorithm
clustering by fast search and find of density peaks
Data field
data field potential entropy
entropy
Optimal threshold value
pattern clustering
Potential entropy
密度
快速搜索
数据场
数据集
经验估计
聚类中心
聚类算法
自动提取
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Summary: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.
ISSN:1022-4653
2075-5597
DOI:10.1049/cje.2016.05.001