期刊
CHINESE JOURNAL OF ELECTRONICS
卷 25, 期 3, 页码 397-402出版社
WILEY
DOI: 10.1049/cje.2016.05.001
关键词
Data field; Potential entropy; Big data clustering; Optimal threshold value; Automatic extraction
资金
- National Natural Science Foundation of China [61173061, 61472039, 71201120]
- Doctoral Fund of Higher Education [20121101110036]
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.
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