data mining analysis using R.

Requirements: you are required to perform meaningful data mining analysis using R. Through this project, you should be able to achieve the following objectives: • Get familiar with R programming language to read data from files, call functions, and print results. • Use several classification, clustering, and frequent pattern mining algorithms. • Understand the steps to perform data analysis tasks using off-the-shelf data mining tools. Tasks: 1. Implement R program to compare two algorithms to perform classification analysis by choosing and utilizing TWO datasets from the data sources listed below. Write a one-page report to do the analysis (detailed requirements of the report see below). 2. Implement R program to compare two clustering algorithms by choosing and utilizing TWO datasets from the data sources listed below. Write a one-page report to do the analysis (detailed requirements of the report see below). 3. Implement R program to test the Apriori algorithm which is used to find the frequent patterns (or item-sets) from transactional data by choosing and utilizing TWO datasets from the data sources listed below. Write a one-page report to do the analysis (detailed requirements of the report see below). 4. Analyze the possible connections between the three different data mining tasks. The analysis needs to be based on your discoveries in the above three tasks. Write a one-page report for this. You need to 1. Choose your own datasets. 2. Write R code to use the algorithms. 3. Write reports to compare the effectiveness and efficiency of the algorithms you used for tasks 1-3. One report for one task. o The report should have a short discussion (2-3 sentences) about the reasons that you choose your data sets.