An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
A multi-objective GRASP for partial classification
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
A novel multi-objective genetic algorithm for clustering
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Dynamic clustering using multi-objective evolutionary algorithm
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
AbYSS: Adapting Scatter Search to Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Clustering is an important task in data mining. However, there are numerous conflicting measurements of what a good clustering solution is. Therefore, clustering is a task that is suitable for a Multi-Objective Evolutionary Algorithm. Mutation operators for these algorithms can be designed to explore a diverse range of solutions or focus upon individual solution quality. We propose using a hybrid technique that generates a wide range of solutions and then improves them with respect to the data. We create an experimental set-up to assess mutation operators with respect to Pareto front quality. Using this set-up we find that mutation operators that mutate solutions with respect to the data perform better but hybrid mutation techniques show promise.