R is a popular programming language used by a growing number of data analysts inside corporations and academia. Students will learn how to apply data mining algorithms in R programming environment.

Part I

Explain each of the following data mining techniques in terms of how the algorithm works, its strength and weakness:

1. 1. Classification
   2. Prediction
   3. Clustering
   4. Association
   5. Correlation analysis

Give an example of each data mining functionality, using a real-life database or data set.

Part II

Using the Ruspini data set provided with the cluster package in R, perform a k-means analysis. Document the findings and justify the choice of k. Hint: Use data (Ruspini) to load the dataset into the R workspace.

While APA style is not required for the body of this assignment, solid academic writing is expected, and documentation of sources should be presented using APA formatting guidelines, which can be found in the APA Style Guide, located in the Student Success Center.

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