Kleanthis Koupidis, Charalampos Bratsas, Jaroslav Kuchar November 9, 2016

#Cluster.OBeu Εstimate and return the necessary parameters for cluster analysis visualizations, used in It involves a set of techniques and algorithms used to find and divide into groups the Budget data of municipalities across Europe, described by the data model.

The available clustering algorithms are hierarchical, kmeans from R base, pam, clara, fuzzy from cluster package and model based algorithms from mclust package. It can be used to find the appropriate clustering algorithm and/or the appropriate clustering number of the input data according to the internal and stability measures from clValid package.

This package can generally be used to estimate clustering parameters, extract and convert them to JSON format and use them as input in a different graphical interface and also can be used in data that are not described by the data model.

You can see detailed information here.

# install Cluster.OBeu- cran stable version
# or
# alternatively install the development version from github

Load library Cluster.OBeu


#Cluster Analysis in a call

cl.analysis can be used to estimate clustering model parameters and/or number of clusters needed for visualization of clusters and other clustering measures as list object.

cluster_data = cl.analysis( city_data, cl.aggregate = "sum", 
                            cl.meth = "pam", clust.numb = NULL, dist = "euclidean", tojson = T) # json string format

jsonlite::prettify(cluster_data) # use prettify of jsonlite library to add indentation to the returned JSON string

#Cluster Analysis on platform is designed to estimate and return the clustering model measures of datasets.

The input data must be a JSON link according to the data model. There are different parameters that a user could specify, e.g. dimensions, measured.dimensions and amounts should be defined by the user, to form the dimensions of the dataset. estimates and returns the json data that are described with the data model, using cl.analysis function.

#Store the link in a variable

clustering =
  json_data =  json_link, 
  dimensions ="administrativeClassification.prefLabel",
  measured.dimensions ="budgetPhase.prefLabel",
  amounts = "amount.sum",
  cl.method = "fanny",
  cl.num = 3
# Pretty output using prettify of jsonlite library
jsonlite::prettify(clustering,indent = 2)