Reference no: EM132494814

DATA

Assignment: Classification Unsupervised

Introduction

Cluster analysis involves grouping things together so that the members of each group are more similar to each other than to members of other groups. There are numerous algorithms or models associated with clustering such as k-means clustering, hierarchical clustering, and density models.

Cluster analysis is popular in market segmentation. For example, the market for a product or service may be segmented into groups of customers or regions that share common interests or are similar in terms of their preferences and socio-economic attributes. An appropriate marketing strategy may then be devised to serve the needs of identified segments better.

This part involves using k-means clustering as a clustering tool to be applied to a data mining study within your domain of interest using R and RStudio.

Steps to Completion
For each study the general procedure is to:
• Review theoretical background based on available resources in the course content
• Select a dataset from the module's recommended datasets list
• Run an analysis, perform evaluation, and capture the results
• Document your findings and analysis in a data mining analytical report

Deliverables
Submit your analysis report by addressing the following critical areas:
• Introduction: give some background and context about the domain of application, provide the rationale for the type of analysis, and state the objective clearly.
• Analysis: describe the data both qualitatively and quantitatively through exploratory analysis, perform necessary preprocessing activities, give some intuition about the algorithm and core parameters, demonstrate the model building steps along with parameter tuning, and explain all your assumptions.
• Result: explain the result and interpret the model output using terms that reflect the application area, perform model evaluation using the appropriate metrics, and leverage visualization.
• Conclusion: summarize your main findings, discuss experimental limitations related to the data and/or implementation of the algorithm, and suggest improvement areas as a potentiation future work.
• Miscellaneous:
o Proofread your report for correct structure, grammar, and spelling
o Follow appropriate APA formatting and provide all references
o Include your R script and extended model outputs in an Appendix section.

Attachment:- Clustering Assignment.rar