Title: Distributed k-Means for Big Data using 'ddR' API
Description: Distributed k-means clustering algorithm written using 'ddR' (Distributed Data Structures) API in the 'ddR' package.

Description: A simple and fast distance-based k-medoids clustering algorithm from
Park and Jun (2009) <doi:10.1016/j.eswa.2008.01.039>. Calculate distances for mixed
variable data such as Gower (1971) <doi:10.2307/2528823>,
Cluster validation applies bootstrap procedure producing a heatmap with a flexible
reordering matrix algorithm such as complete, ward, or average linkages.

Description: Algorithms of distance-based k-medoids clustering:
simple and fast k-medoids (Park and Jun, 2009) <doi:10.1016/j.eswa.2008.01.039>,
ranked k-medoids (Zadegan et al., 2013) <doi:10.1016/j.knosys.2012.10.012>, and
Calculate distances for mixed variable data such as

Calculate distances for mixed variable data such as Gower, Podani, Wishart (2003) <doi:10.1007/978-3-642-55721-7_23>,

simple and fast k-medoids, ranked k-medoids, and increasing number
of clusters in k-medoids. Calculate distances for mixed variable
data such as Gower, Podani, Wishart, Huang, Harikumar-PV, and
Ahmad-Dey. Cluster validations apply internal and relative
criteria. The internal criteria include silhouette index and
shadow values. The relative criterium applies bootstrap procedure
producing a heatmap with a flexible reordering matrix algorithm
such as ward, complete, or centroid linkages. The cluster result
can be plotted in a marked barplot or pca biplot.

Description: Algorithms of distance-based k-medoids clustering: simple and fast
k-medoids, ranked k-medoids, and increasing number of clusters in k-medoids.
Calculate distances for mixed variable data such as Gower, Podani, Wishart,
Huang, Harikumar-PV, and Ahmad-Dey. Cluster validation applies internal and
relative criteria. The internal criteria includes silhouette index and shadow
values. The relative criterium applies bootstrap procedure producing a heatmap
with a flexible reordering matrix algorithm such as complete, ward, or average
linkages. The cluster result can be plotted in a marked barplot or pca biplot.