Data Analysis Project: Classification, Clustering, and Association

This project focuses on developing and implementing data classification and clustering using R and SAS Enterprise. The project utilizes R code for dataset classification, with outputs visualized in a tree structure. The dataset includes fields such as customer ID, stock code, invoice number, description, country, and unit price. The project explores classification techniques using R code, leveraging SAS Enterprise for data import and analysis. Clustering analysis, including distribution and density-based, hierarchical, and centroid clustering, is performed. Association rule mining is applied to identify relationships within the dataset, with results presented in a graphical format. The project successfully demonstrates the application of these techniques to analyze and interpret the given dataset.

Contents
1. Introduction...................................................................................................................................3
2. SAS enterprise...............................................................................................................................3
3. R Code...........................................................................................................................................3
4. Classification On Dataset..............................................................................................................3
5. Clustering Analysis........................................................................................................................4
6. Association Rule Mining...............................................................................................................8
7. Conclusion...................................................................................................................................12
8. References...................................................................................................................................13

1. Introduction
Project objects are to develop the clustering and classifying the dataset using R and SAS
enterprise code. The R code can be used for the classifying the dataset and find the output can
be displayed on the tree structure. The data can contains the set of fields includes are
customer id, stock code, invoice no, description, country and unit price dataset. Same dataset
can be using for the association rules it will be investigated.
2. SAS enterprise
The association rules can be designed on the SAS enterprise and the dataset can be
implementing on the R code. The accessing of the each data set can be considered as data
preparation. The SAS enterprise can be allowed the R code and used for modelling predictive
mark-up language (Classification, 2014). The executing the code and the result can be
displayed on the graphical visualization format.
3. R Code
The R code using to classify the dataset. The SAS enterprise can be access and import
the dataset after can be implementing on the tree. The R code can be used for apply the given
data set of the association rules to find the tree structure.
4. Classification On Dataset
The dataset classification can be implementing on the R code. Access on all the dataset
can be import on the SAS enterprise. The packages can be locating in R code and open the
new file can be creating the tree. Once you start your R program, there are datasets available
within R along with loaded packages. You can list the data sets by their clustering and
classification and then load a data set into memory to be used in the statistical analysis. The
command data will be load on the dataset into current memory. The R classification can be
display the output structure on decision tree. The R code can be works for both types input
and output the variables (Celebi, 2016). The objective of the data classification can be
displayed on the output on tree that can be contains the parent tree of the stock, child code in
the quatity, customer id and unit price. The dataset can be contains the parent and child node
and result is displayed by the visualization.

5. Clustering Analysis
The clustering is the one of the concept of analyzing on the statistical data. The retailed
on the data can be displayed on the visualized format. The clustering can be used for
analyzing of the visualized data it used for the association rule can be follows the various
type of clustering and can implementing the R Code of SAS enterprise. The implementing of
the various types of the clustering that are includes are distribution and density based

clustering, hierarchical and centroid clustering. These reports can be implementing on the
hierarchical clustering on the SAS enterprise.

6. Association Rule Mining
The machine learning of the dataset can be used for the association data can be followed
on the two parts that are includes the consequent and antecedent. This report of the dataset
items can be find the antecedent format. The dataset can be used for the combination of
consequent and antecedent (Tripathi, 2015). The search of the data can be followed by the
some rules at the same time to identify the relationship of important dataset. In this part can
be apply dataset implementing on the R code and finally displayed on the graphical format
dataset.