Open AccessClustering by Fast Search and Find of Density Peaks with Data Field
Author(s) -
Wang Shuliang,
Wang Dakui,
Li Caoyuan,
Li Yan,
Ding Gangyi
Publication year - 2016
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2016.05.001
Subject(s) - cluster analysis , computer science , threshold limit value , principle of maximum entropy , entropy (arrow of time) , data mining , value (mathematics) , field (mathematics) , pattern recognition (psychology) , algorithm , mathematics , artificial intelligence , machine learning , physics , medicine , environmental health , quantum mechanics , pure mathematics
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.