Distance measures (GDM1, GDM2,	Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap,	Davies-Bouldin),	data normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method,	replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data).
(MILLIGAN, G.W., COOPER, M.C. (1985) <doi:10.1007/BF02294245>,
HUBERT, L., ARABIE, P. (1985) <doi:10.1007%2FBF01908075>,
RAND, W.M. (1971) <doi:10.1080/01621459.1971.10482356>,
JAJUGA, K., WALESIAK, M. (2000) <doi:10.1007/978-3-642-57280-7_11>,
MILLIGAN, G.W., COOPER, M.C. (1988) <doi:10.1007/BF01897163>,
JAJUGA, K., WALESIAK, M., BAK, A. (2003) <doi:10.1007/978-3-642-55721-7_12>,
DAVIES, D.L., BOULDIN, D.W. (1979) <doi:10.1109/TPAMI.1979.4766909>,
CALINSKI, T., HARABASZ, J. (1974) <doi:10.1080/03610927408827101>,
HUBERT, L. (1974) <doi:10.1080/01621459.1974.10480191>,
TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) <doi:10.1111/1467-9868.00293>,
BRECKENRIDGE, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>,
WALESIAK, M., DUDEK, A. (2008) <doi:10.1007/978-3-540-78246-9_11>).

ade4, e1071, rgl, R2HTML, grDevices, graphics, stats, utils

ade4, e1071, R2HTML, grDevices, graphics, stats, utils

Performs Tests for Cluster Tendency of a Data Set
c(person("Zachariah", "Neville", email = "z.neville@stat.fsu.edu", role = c("aut", "cre")), person("Naomi", "Brownstein", email = "Naomi.Brownstein@moffitt.org", role = "aut"))
Test for cluster tendency (clusterability) of a data set.
The methods implemented - reducing the data set to a single dimension using principal component analysis or computing
pairwise distances, and performing a multimodality test like the Dip Test or Silverman's Critical Bandwidth Test -
are described in Adolfsson, Ackerman, and Brownstein (2019) <doi:10.1016/j.patcog.2018.10.026>. Such methods can inform whether clustering algorithms
are appropriate for a data set.
Zachariah Neville [aut, cre],
Naomi Brownstein [aut]