A multi-objective genetic algorithm with fuzzy c-means for automatic data clustering

2014 ◽  
Vol 24 ◽  
pp. 679-691 ◽  
Author(s):  
Siripen Wikaisuksakul
Keyword(s):  
Genetic Algorithm ◽  
Data Clustering ◽  
Automatic Data ◽  
Multi Objective ◽  
Fuzzy C Means ◽  
Multi Objective Genetic Algorithm

Multi-Objective Genetic Algorithm for Robust Clustering with Unknown Number of Clusters

2012 ◽  
Vol 3 (1) ◽  
pp. 1-20
Author(s):  
Amit Banerjee
Keyword(s):  
Genetic Algorithm ◽  
Data Clustering ◽  
Optimal Number ◽  
Least Trimmed Squares ◽  
Cluster Assignment ◽  
Objective Criterion ◽  
Number Of Clusters ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Optimal Number Of Clusters

In this paper, a multi-objective genetic algorithm for data clustering based on the robust fuzzy least trimmed squares estimator is presented. The proposed clustering methodology addresses two critical issues in unsupervised data clustering – the ability to produce meaningful partition in noisy data, and the requirement that the number of clusters be known a priori. The multi-objective genetic algorithm-driven clustering technique optimizes the number of clusters as well as cluster assignment, and cluster prototypes. A two-parameter, mapped, fixed point coding scheme is used to represent assignment of data into the true retained set and the noisy trimmed set, and the optimal number of clusters in the retained set. A three-objective criterion is also used as the minimization functional for the multi-objective genetic algorithm. Results on well-known data sets from literature suggest that the proposed methodology is superior to conventional fuzzy clustering algorithms that assume a known value for optimal number of clusters.

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