cl.analysis {Cluster.OBeu}

Cluster analusis

Description

Clustering Analysis for OBEU datasets.

Usage

cl.analysis(cl.data, cl_feature = NULL, amount = NULL, cl.aggregate = "sum",
cl.meth = NULL, clust.numb = NULL, dist = "euclidean", tojson = FALSE)

Arguments

cl.data

The input data

cl_feature

The feature to be clustered (nominal variables)

amount

The numeric variables

cl.aggregate

Select a different aggregation in case of filtering the input data

cl.meth

The clustering method algorithm

clust.numb

The number of clusters

dist

The distance metric

tojson

If TRUE the results are returned in json format, default returns a list

Details

There are different clustering models to be selected through an evaluation process. The user should define the cl_feature, cl.aggregate and amount parameters to form the structure of cluster data. The clustering algorithm, the number of clusters and the distance metric of the clustering model are set to the best selection using internal and stability measures. The end user can also interact with the cluster analysis and these parameters by specifying the cl.method, cl.num and cl.dist parameters respectively.

Value

The final returns are the parameters needed for visualizing the cluster data depending on the selected algorithm and the specification parameters, as long as some comparison measure matrices.

• cluster.method - Label of the clustering algorithm

• raw.data - Input data

• data.pca - The principal components to visualize the input data

• modelparam - The results of this parameter depend of the selected clustering model

• compare - Clustering measures

Author(s)

Kleanthis Koupidis, Jaroslav Kuchar

See Also

cl.features, clValid, diana, agnes, pam, clara, fanny, Mclust

Examples

cl.analysis(city_data, cl.meth = "pam", clust.numb = 3)