Int J Neural Syst - A genetic graph-based approach for partitional clustering.

Tópicos

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Resumo

Clustering is one of the most versatile tools for data analysis. In the recent 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 (SC) algorithm, which is based on graph cut: It initially generates a similarity graph using a distance measure and then studies its graph spectrum to find the best cut. This approach is sensitive to the parameters of the metric, and a correct parameter choice is critical to the quality of the cluster. This work proposes a new algorithm, inspired by SC, that reduces the parameter dependency while maintaining the quality of the solution. The new algorithm, named genetic graph-based clustering (GGC), takes an evolutionary approach introducing a genetic algorithm (GA) to cluster the similarity graph. The experimental validation shows that GGC increases robustness of SC and has competitive performance in comparison with classical clustering methods, at least, in the synthetic and real dataset used in the experiments.

Resumo Limpo

cluster one versatil tool data analysi recent year cluster seek continu data opposit classic centroidbas approach attract increas research interest challeng problem remark practic interest popular continu cluster method spectral cluster sc algorithm base graph cut initi generat similar graph use distanc measur studi graph spectrum find best cut approach sensit paramet metric correct paramet choic critic qualiti cluster work propos new algorithm inspir sc reduc paramet depend maintain qualiti solut new algorithm name genet graphbas cluster ggc take evolutionari approach introduc genet algorithm ga cluster similar graph experiment valid show ggc increas robust sc competit perform comparison classic cluster method least synthet real dataset use experi

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