Density-sensitive fuzzy kernel maximum entropy clustering algorithm

Maximum entropy clustering algorithm (ME) has lately received great attention for its high performance in large-scale data clustering and simplicity in implementation. However, previous studies have demonstrated that different clusters obtained by traditional ME tend to converge to the same one duri...

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Vydané v:	Knowledge-based systems Ročník 166; s. 42 - 57
Hlavní autori:	Tao, Xinmin, Wang, Ruotong, Chang, Rui, Li, Chenxi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 15.02.2019 Elsevier Science Ltd
Predmet:	Algorithms Bias Clustering Convergence Datasets Density Digits Entropy Experiments Function Fuzzy sets Handwriting Implementation Iterative methods Kernel functions Maximum entropy Maximum entropy clustering algorithm Normal distribution Object recognition Regularization Relative density-based weight Robustness Simplicity Relative density-based weight Maximum entropy clustering algorithm Robustness Clustering
ISSN:	0950-7051, 1872-7409
On-line prístup:	Získať plný text
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