A multi-objective genetic graph-based clustering algorithm with memory optimization
EntityUAM. Departamento de Ingeniería Informática
PublisherInstitute of Electrical and Electronics Engineers
10.1109/CEC.2013.65579582013 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2013. 3174 - 3181
ISBN978-1-4799-0452-5 (online); 978-1-4799-0453-2 (print)
Funded byThis work has been partly supported by: Spanish Ministry of Science and Education under project TIN2010-19872.
SubjectsData analysis; Data mining; Expectation-maximisation algorithm; Genetic algorithms; Graph theory; Informática
NotePersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. H. D. Menéndez, D. F. Barrero, and D. Camacho, "A multi-objective genetic graph-based clustering algorithm with memory optimization", in 2013 IEEE Congress on Evolutionary Computation (CEC), 2013, pp. 3174 - 3181
Rights© 2013 IEEE
Clustering is one of the most versatile tools for data analysis. Over the last few years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the Spectral Clustering algorithm, which is based on graph cut: it initially generates a Similarity Graph using a distance measure and then uses its Graph Spectrum to find the best cut. Memory consuption is a serious limitation in that algorithm: The Similarity Graph representation usually requires a very large matrix with a high memory cost. This work proposes a new algorithm, based on a previous implementation named Genetic Graph-based Clustering (GGC), that improves the memory usage while maintaining the quality of the solution. The new algorithm, called Multi-Objective Genetic Graph-based Clustering (MOGGC), uses an evolutionary approach introducing a Multi-Objective Genetic Algorithm to manage a reduced version of the Similarity Graph. The experimental validation shows that MOGGC increases the memory efficiency, maintaining and improving the GGC results in the synthetic and real datasets used in the experiments. An experimental comparison with several classical clustering methods (EM, SC and K-means) has been included to show the efficiency of the proposed algorithm.
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