Open AccessGravitational Inspired Spectral Clustering with Constraint
Author(s) -
Sun Liping,
Luo Yonglong,
Zheng Xiaoyao,
Lv Jun
Publication year - 2015
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.2015.07.008
Subject(s) - constraint (computer aided design) , cluster analysis , spectral clustering , computer science , gravitation , mathematics , artificial intelligence , physics , classical mechanics , geometry
Spectral clustering with pair wise constraints (i.e. must link and cannot link) has been a hot topic in the machine learning community in recent years. Its performances are significantly influenced by utilizing the constraints. To make full use of the constraints’ effect, pairwise constraints are integrated into an affinity matrix based onthe gravitational method. In the data set as input, each point has mass property, and interacts with each other according to the universal law of gravitation. A Gravitational inspired constrained spectral clustering (GCSC) algorithmis proposed in this paper. Our algorithm is evaluated on multiple benchmark classification datasets. Compared with the existing approaches, experimental results demonstrate the effectiveness of our presented algorithm.