Scaling-up multiobjective evolutionary clustering algorithms using stratification

Multiobjective evolutionary clustering algorithms are based on the optimization of several objective functions that guide the search following a cycle based on evolutionary algorithms. Their capabilities allow them to find better solutions than with conventional clustering algorithms when more than one criterion is necessary to obtain understandable patterns from the data. However, these kind of techniques are expensive in terms of computational time and memory usage, and specific strategies are required to ensure their successful scalability when facing large-scale data sets. This work proposes the application of a data subset approach for scaling-up multiobjective clustering algorithms and it also analyzes the impact of three stratification methods. The experiments show that the use of the proposed data subset approach improves the performance of multiobjective evolutionary clustering algorithms without considerably penalizing the accuracy of the final clustering solution.