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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.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.

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.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.

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.