Detailed Question:

Using software R stated in pdf file and make a report/log with screenshots and explanation. The other pdf file tutorial is for reference/help

Overview: In this assignment, you will explore three popular clustering techniques using R.

The objectives are 1) To learn how to cluster data 2) To learn what are some of the issues when dealing with clustering and 3) To be able to make some conclusions (and personal opinions) about your findings.

Part 1: Downloading and Installing R

Part 2a: The Assignment

Work through the following section of the R_Cluster Analysis_tutorial.pdf file found with the assignment file, using the mtcars dataset:

1. Data Preparation

2. Determining the number of clusters (please also read: http://en.wikipedia.org/wiki/Determining_the_number_of_clusters_in_a_data_set)

3. Partitioning

4. Hierarchical Agglomerative

5. Model Based

Use summary(Mclust(mydata), mydata)$classification to find out which data pattern is assigned to which cluster. Find out how this can be done for 3 and 4, if the assigned clusters are not shown directly.

6. Plotting Cluster Solutions

7. Validating cluster solutions

Part 2b: Investigations and Questions:

1) Using the within sum of square graph in Part 2a Step2, how do you determine the number of clusters?

2) Are the solutions from K-means always the same? Why?

3) Compare the clustering results from Part 2a: Step: 3, 4 and 5. Explain your findings.

4) What are your personal thoughts about the three different clustering algorithms?

Attachment:- R_cluster-analysis_tutorial.pdf