DocumentCode
2097903
Title
Theoretical Derivations of Min-Max Information Clustering Algorithm
Author
Zhang, Chi ; Yang, Xu-Lei ; Zhao, Guanzhou ; Wan, Jie
Author_Institution
Ningbo Inst. of Technol., Zhejiang Univ., Ningbo, China
fYear
2011
fDate
17-18 Sept. 2011
Firstpage
128
Lastpage
131
Abstract
The min-max information (MMI) clustering algorithm was proposed in [8] for robust detection and separation of spherical shells. In current paper, we make efforts to revisit the proposed MMI algorithm theoretically and practically. Firstly, we present the theoretical derivations of the MMI clustering algorithm, i.e., the detailed derivations of the minimization and maximization optimization of the mutual information. Secondly, several insights on the selection of the pruning parameter λ are also discussed in this paper.
Keywords
optimisation; pattern clustering; maximization optimization; min-max information clustering algorithm; minimization optimization; pruning parameter; spherical shells; Clustering algorithms; Equations; Minimization; Mutual information; Noise measurement; Optimization; Robustness; Min-Max Informations Optimization; Mutual Information; Robust Clustering; Spherical Shells Detection;
fLanguage
English
Publisher
ieee
Conference_Titel
Internet Computing & Information Services (ICICIS), 2011 International Conference on
Conference_Location
Hong Kong
Print_ISBN
978-1-4577-1561-7
Type
conf
DOI
10.1109/ICICIS.2011.38
Filename
6063210
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