info:eu-repo/semantics/article
An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters
Fecha
2018-08Registro en:
Carballido, Jessica Andrea; Latini, Macarena Anahí; Ponzoni, Ignacio; Cecchini, Rocío Luján; An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters; Elsevier; Electronic Notes in Discrete Mathematics; 69; 8-2018; 229-236
1571-0653
CONICET Digital
CONICET
Autor
Carballido, Jessica Andrea
Latini, Macarena Anahí
Ponzoni, Ignacio
Cecchini, Rocío Luján
Resumen
One of the main problems being faced at the time of performing data clustering consists in the deteremination of the best clustering method together with defining the ideal amount (k) of groups in which these data should be separated. In this paper, a preliminary approximation of a clustering recommender method is presented which, starting from a set of standardized data, suggests the best clustering strategy and also proposes an advisable k value. For this aim, the algorithm considers four indices for evaluating the final structure of clusters: Dunn, Silhouette, Widest Gap and Entropy. The prototype is implemented as a Genetic Algorithm in which individuals are possible configurations of the methods and their parameters. In this first prototype, the algorithm suggests between four partitioning methods namely K-means, PAM, CLARA and, Fanny. Also, the best set of parameters to execute the suggested method is obtained. The prototype was developed in an R environment, and its findings could be corroborated as consistent when compared with a combination of results provided by other methods with similar objectives. The idea of this prototype is to serve as the initial basis for a more complex framework that also incorporates the reduction of matrices with vast numbers of rows.