Strategic oscillation for the balanced minimum sum-of-squares clustering problem

2021 ◽  
Author(s):  
R. Martín-Santamaría ◽  
J. Sánchez-Oro ◽  
S. Pérez-Peló ◽  
A. Duarte
Keyword(s):  
Sum Of Squares ◽  
Clustering Problem ◽  
Strategic Oscillation

CLUSTERING USING AN IMPROVED HYBRID GENETIC ALGORITHM

2007 ◽  
Vol 16 (06) ◽  
pp. 919-934
Author(s):  
YONGGUO LIU ◽  
XIAORONG PU ◽  
YIDONG SHEN ◽  
ZHANG YI ◽  
XIAOFENG LIAO
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Sum Of Squares ◽  
Clustering Methods ◽  
Clustering Problem ◽  
Mutation Operation ◽  
Iteration Methods ◽  
Genetic Clustering ◽  
The Individual

In this article, a new genetic clustering algorithm called the Improved Hybrid Genetic Clustering Algorithm (IHGCA) is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGCA, the improvement operation including five local iteration methods is developed to tune the individual and accelerate the convergence speed of the clustering algorithm, and the partition-absorption mutation operation is designed to reassign objects among different clusters. By experimental simulations, its superiority over some known genetic clustering methods is demonstrated.

A tabu search approach for the minimum sum-of-squares clustering problem

2008 ◽  
Vol 178 (12) ◽  
pp. 2680-2704 ◽  
Author(s):  
Yongguo Liu ◽  
Zhang Yi ◽  
Hong Wu ◽  
Mao Ye ◽  
Kefei Chen
Keyword(s):  
Tabu Search ◽  
Sum Of Squares ◽  
Clustering Problem ◽  
Search Approach

scholarly journals Sum-of-squares clustering on networks

2011 ◽  
Vol 21 (2) ◽  
pp. 157-161 ◽  
Author(s):  
Emilio Carrizosa ◽  
Nenad Mladenovic ◽  
Raca Todosijevic
Keyword(s):  
Data Analysis ◽  
Sum Of Squares ◽  
Continuous Location ◽  
Clustering Problem ◽  
Clustering Data ◽  
Set Of Points ◽  
First Time ◽  
Optimal Cluster

Finding p prototypes by minimizing the sum of the squared distances from a set of points to its closest prototype is a well-studied problem in clustering, data analysis and continuous location. In this note, this very same problem is addressed assuming, for the first time, that the space of possible prototype locations is a network. We develop some interesting properties of such clustering problem. We also show that optimal cluster prototypes are not necessary located at vertices of the network.

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