Author: Zhicheng Du,Wangjian Zhang, Yuantao Hao

Author: Zhicheng Du, Wangjian Zhang, Yuantao Hao

Title: Genetic Approach to Maximize Clustering Criterion
Authors@R: c(person(given = "Jairson", family ="Rodrigues", role = c("aut", "cre"),
email = "jairson.rodrigues@univasf.edu.br", comment = c(ORCID = "0000-0003-1176-3903")),
person(given = "Germano", family ="Vasconcelos", role = c("aut", "ths"),
person(given = "Renato", family = "Tin\'{o}s", role = c("aut", "rev"),
email = "rtinos@ffclrp.usp.br", comment = c(ORCID = "0000-0003-4027-8851")))
Description: An evolutionary approach to performing hard partitional clustering. The algorithm uses genetic operators guided by information about the quality of individual partitions. The method looks for the best barycenters/centroids configuration (encoded as real-value) to maximize or minimize one of the given clustering validation criteria: Silhouette, Dunn Index, C-Index or Calinski-Harabasz Index. As many other clustering algorithms, 'gama' asks for k: a fixed a priori established number of partitions. If the user does not know the best value for k, the algorithm estimates it by using one of two user-specified options: minimum or broad. The first method uses an approximation of the second derivative of a set of points to automatically detect the maximum curvature (the 'elbow') in the within-cluster sum of squares error (WCSSE) graph. The second method estimates the best k value through majority voting of 24 indices. One of the major advantages of 'gama' is to introduce a bias to detect partitions which attend a particular criterion. References: Scrucca, L. (2013) <doi:10.18637/jss.v053.i04>; CHARRAD, Malika et al. (2014) <doi:10.18637/jss.v061.i06>; Tsagris M, Papadakis M. (2018) <doi:10.7287/peerj.preprints.26605v1>; Kaufman, L., & Rousseeuw, P. (1990, ISBN:0-47 1-73578-7).
Imports: ArgumentCheck, cluster, clusterCrit, NbClust, GA, ggplot2,
Author: Jairson Rodrigues [aut, cre] (<https://orcid.org/0000-0003-1176-3903>),

Title: Data for "GAMs: an introduction with R"
Description: Data sets used in the book "Generalized Additive Models:
An introduction with R", Wood (2006) CRC.
Packaged: Thu Dec  1 18:24:38 2005; simon