Subject Code : INFS5102
Assignment Task:
INFS5102: Cluster Analysis Assignment Help 

Instructions
Background
You are given a data set with 5820 samples and 33 attributes. The data set is in the “Assignment 1 - specification and data” folder on Learnonline course site.
Each sample of the given data set records a student’s evaluation scores on 28 evaluation questions for a course and the instructor. Apart from the 28 attributes corresponding to the 28 evaluation questions, the data set has another 5 attributes: instructor ID, class code, number of repetitions of the course, level of attendance, and level of difficulty of the course (as perceived by the student).

The given data set is sourced from the UCI Machine Learning Repository (with slight modifications), and a more detailed description of the data set is available at:
http://archive.ics.uci.edu/ml/datasets/Turkiye+Student+Evaluation For this assignment, you are required to use SAS Enterprise Miner to conduct cluster analysis
on the given data set and write a report about your analysis.

Cluster Analysis using SAS Enterprise Miner
Before clustering the data set, explore the data set and preprocess the data set if necessary, either using the preprocessing functions available within the Cluster tool or similar functions provided by other tools of SAS Enterprise Miner. When running the Cluster tool & the Segment Profile tool:

• Firstly set the number of clusters by yourself, e.g. to 6, then reduce the number of clusters to 5, ..., till 2. Alternatively, you can start with the smallest number, 2, and then
increase the number of clusters.

• Next use the Automatic option to let SAS Enterprise Miner determine the number of clusters automatically.

• Analyse and compare the results obtained in different cases.

Report
You need to include the following contents in the report:
1. (20/100) A description in your own words to explain what is cluster analysis and how different categories of clustering algorithms (for example, partition-based, density-based, etc.) works, assuming that your reader does not know about clustering. Use easy-to-understand language and examples in your description. Use figures and/or tables to help with the description.

2. (40/100) A description of the experiments that you have conducted in SAS Enterprise Miner. You can structure the description into two parts:

• Data exploration and preprocessing:
o What data exploration and preprocessing that you have done and how they are done. Use diagrams/screenshots to help with the presentation of the steps.
o Justification of the need of the data preprocessing, explain why you have performed each specific preprocessing step.

• Cluster analysis:
For each of the experiments you have done,
o Describe the experiment settings (e.g. the clustering algorithm you have chosen to use, the parameters of the algorithm)
o Describe the results obtained with the different settings (i.e. the results of running the Cluster node and the Segment Profile node with the settings). Use diagrams/screenshots to help with the presentation of the results, and also provide text description of the results.

3. (40/100) A discussion of the results, including (but not limited to)
• Comparison of the results obtained using different clustering algorithms. Explain which clustering algorithm and distance metric is more suitable for the given data set and explain the reasons.

• For each specific clustering algorithm, comparison of the results obtained using parameters (e.g., for k-means, a comparison of different user specified numbers of
clusters and the number of clusters determined automatically by Enterprise Miner). Base the discussion on the results of running the Cluster and Segment Profile nodes,
and explain why one clustering may be better than or similar to another.

• Interpretation and discussion of the results. Based on the clustering results, provide insights into the data set and the practical problem. For example, what is the common characteristics of the samples in the same cluster and how the different clusters are distinct from each other? What implications do these commonalities and difference provide regarding the practical problem?

• Pros and cons of using SAS Enterprise Minder for analyzing the given data set, and the lessons learned. To support your discussion, you may relate the results to your study experience to provide evidence. The researchers who donated the original data set did some preliminary analysis of the data set1 . You may refer to their work to get some hints as to how to start your discussion (please note that, if you refer to their work, pay attention to and describe how your results are similar and/or different from theirs and why)

 

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  • Posted on : November 13th, 2018

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