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Multi-objective differential evolution for automatic clustering with application to micro-array data analysis.

Suresh K, Kundu D, Ghosh S, Das S, Abraham A, Han SY - Sensors (Basel) (2009)

Bottom Line: It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized.A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE.The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II) and Multi-Objective Clustering with an unknown number of Clusters K (MOCK).

View Article: PubMed Central - PubMed

Affiliation: Dept. of Electronics and Telecommunication Engg, Jadavpur University, Kolkata, India; E-Mails: kaushik_s1988@yahoo.com ; kundu.debarati@gmail.com ; sayan88tito@gmail.com ; swagatamdas19@yahoo.co.in.

ABSTRACT
This paper applies the Differential Evolution (DE) algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE. The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II) and Multi-Objective Clustering with an unknown number of Clusters K (MOCK). Experimental results using six artificial and four real life datasets of varying range of complexities indicate that DE holds immense promise as a candidate algorithm for devising MO clustering schemes.

No MeSH data available.


Clustering result for artificial dataset_1.
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f4-sensors-09-03981: Clustering result for artificial dataset_1.

Mentions: The results listed in Tables 2 to 4 indicate that there is always one or more multi-objective DE variant that beats the NSGA II or MOCK in terms of mean Silhouette index and adjusted Rand index in a statistically significant fashion. The six unlabelled artificial datasets and the corresponding clustered data with the best performing algorithm (which happens to be one of the two multi-objective DE variants) have been depicted in Figures 4 to 9.


Multi-objective differential evolution for automatic clustering with application to micro-array data analysis.

Suresh K, Kundu D, Ghosh S, Das S, Abraham A, Han SY - Sensors (Basel) (2009)

Clustering result for artificial dataset_1.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3297137&req=5

f4-sensors-09-03981: Clustering result for artificial dataset_1.
Mentions: The results listed in Tables 2 to 4 indicate that there is always one or more multi-objective DE variant that beats the NSGA II or MOCK in terms of mean Silhouette index and adjusted Rand index in a statistically significant fashion. The six unlabelled artificial datasets and the corresponding clustered data with the best performing algorithm (which happens to be one of the two multi-objective DE variants) have been depicted in Figures 4 to 9.

Bottom Line: It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized.A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE.The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II) and Multi-Objective Clustering with an unknown number of Clusters K (MOCK).

View Article: PubMed Central - PubMed

Affiliation: Dept. of Electronics and Telecommunication Engg, Jadavpur University, Kolkata, India; E-Mails: kaushik_s1988@yahoo.com ; kundu.debarati@gmail.com ; sayan88tito@gmail.com ; swagatamdas19@yahoo.co.in.

ABSTRACT
This paper applies the Differential Evolution (DE) algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE. The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II) and Multi-Objective Clustering with an unknown number of Clusters K (MOCK). Experimental results using six artificial and four real life datasets of varying range of complexities indicate that DE holds immense promise as a candidate algorithm for devising MO clustering schemes.

No MeSH data available.