Determining the Number of Clusters using Neural Network and Max Stable Set Problem

Procedia Computer Science ◽

10.1016/j.procs.2018.01.093 ◽

2018 ◽

Vol 127 ◽

pp. 16-25 ◽

Cited By ~ 2

Author(s):

Awatif Karim ◽

Chakir Loqman ◽

Jaouad Boumhidi

Keyword(s):

Neural Network ◽

Stable Set ◽

Number Of Clusters ◽

Stable Set Problem

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A polyhedral study of the maximum stable set problem with weights on vertex-subsets

Discrete Applied Mathematics ◽

10.1016/j.dam.2015.05.032 ◽

2016 ◽

Vol 210 ◽

pp. 223-234

Author(s):

Manoel Campêlo ◽

Victor A. Campos ◽

Ricardo C. Corrêa ◽

Diego Delle Donne ◽

Javier Marenco ◽

...

Keyword(s):

Stable Set ◽

Maximum Stable Set ◽

Polyhedral Study ◽

Stable Set Problem

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Strong lift-and-project cutting planes for the stable set problem

Mathematical Programming ◽

10.1007/s10107-012-0513-3 ◽

2012 ◽

Vol 141 (1-2) ◽

pp. 165-192 ◽

Cited By ~ 13

Author(s):

Monia Giandomenico ◽

Fabrizio Rossi ◽

Stefano Smriglio

Keyword(s):

Cutting Planes ◽

Stable Set ◽

Strong Lift ◽

Stable Set Problem

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An Optimizing Method of Competitive Neural Network

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.467-469.894 ◽

2011 ◽

Vol 467-469 ◽

pp. 894-899

Author(s):

Hong Men ◽

Hai Yan Liu ◽

Lei Wang ◽

Yun Peng Pan

Keyword(s):

Neural Network ◽

Network Structure ◽

Clustering Analysis ◽

Learning Rate ◽

Optimum Number ◽

Number Of Clusters ◽

Competitive Neural Network ◽

Output Neurons ◽

Simulation Results ◽

Strong Ability

This paper presents an optimizing method of competitive neural network(CNN):During clustering analysis fixed on the optimum number of output neurons according to the change of DB value，and then adjusted connected weight including increasing ,dividing , delete. Each neuron had the different variety trend of learning rate according with the change of the probability of neurons. The optimizing method made classification more accurate. Simulation results showed that optimized network structure had a strong ability to adjust the number of clusters dynamically and good results of classification.

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