Automatic Clustering Using a Genetic Algorithm with New Solution Encoding and Operators

2014 ◽  
pp. 92-103 ◽  
Author(s):  
Carolina Raposo ◽  
Carlos Henggeler Antunes ◽  
João Pedro Barreto
Keyword(s):  
Genetic Algorithm ◽  
Automatic Clustering

A novel genetic algorithm for automatic clustering

2004 ◽  
Vol 25 (2) ◽  
pp. 173-187 ◽  
Author(s):  
Gautam Garai ◽  
B.B Chaudhuri
Keyword(s):  
Genetic Algorithm ◽  
Automatic Clustering

A two-stage genetic algorithm for automatic clustering

2012 ◽  
Vol 81 ◽  
pp. 49-59 ◽  
Author(s):  
Hong He ◽  
Yonghong Tan
Keyword(s):  
Genetic Algorithm ◽  
Two Stage ◽  
Automatic Clustering

scholarly journals Dynamic niching genetic algorithm with data attraction for automatic clustering

2009 ◽  
Vol 14 (6) ◽  
pp. 718-724 ◽  
Author(s):  
Dongxia Chang ◽  
Xianda Zhang
Keyword(s):  
Genetic Algorithm ◽  
Automatic Clustering

An automatic clustering for interval data using the genetic algorithm

2020 ◽  
Author(s):  
Tai Vovan ◽  
Dinh Phamtoan ◽  
Le Hoang Tuan ◽  
Thao Nguyentrang
Keyword(s):  
Genetic Algorithm ◽  
Interval Data ◽  
Automatic Clustering

scholarly journals A genetic algorithm using Calinski-Harabasz index for automatic clustering problem

2020 ◽  
Vol 12 (3) ◽  
pp. 97-106
Author(s):  
Suzane Pereira Lima ◽  
Marcelo Dib Cruz
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Cluster Validity ◽  
Number Of Clusters ◽  
Correct Number ◽  
Clustering Problem ◽  
Automatic Clustering ◽  
Optimal Number Of Clusters

Data clustering is a technique that aims to represent a dataset in clusters according to their similarities. In clustering algorithms, it is usually assumed that the number of clusters is known. Unfortunately, the optimal number of clusters is unknown for many applications. This kind of problem is called Automatic Clustering. There are several cluster validity indexes for evaluating solutions, it is known that the quality of a result is influenced by the chosen function. From this, a genetic algorithm is described in this article for the resolution of the automatic clustering using the Calinski-Harabasz Index as a form of evaluation. Comparisons of the results with other algorithms in the literature are also presented. In a first analysis, fitness values equivalent or higher are found in at least 58% of cases for each comparison. Our algorithm can also find the correct number of clusters or close values in 33 cases out of 48. In another comparison, some fitness values are lower, even with the correct number of clusters, but graphically the partitioning are adequate. Thus, it is observed that our proposal is justified and improvements can be studied for cases where the correct number of clusters is not found.

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