Association Rules Mining

Data mining is used to locate beneficial information by finding anomalies, patterns, or correlations within data. Association rules mining uses “if-then” statements to show the most important relationships between data. Some real-world examples of its use include medical diagnosis, purchasing patterns, consumer website usages, or content recommendation engines.

To perform association rules analysis/mining, complete the following:

Access the “UCI Machine Learning Repository,” https://archive.ics.uci.edu/datasets

Note: There are about 120 data sets that are suitable for use in a clustering task. For this part of the exercise, you must choose two of these datasets, provided they include at least 10 attributes and 10,000 instances.

1. Ensure that the data sets are suitable for clustering using this method.
2. You may search for data in other repositories, such as Data.gov or Kaggle.

For your selected dataset, build a clustering model as follows:

1. Explain the dataset and the type of information you wish to extract. Recall that the dataset must consist of transactions of the form If {x1, x2, …, xn} then {y1, y2, …, yk}.
2. Explain the Apriori algorithm and how you will be using it in your analysis (list the steps, the intuition behind the mathematical representation, and address its assumptions).
3. Identify the appropriate software packages.
4. Preprocess the data, describe their characteristics, and visualize key characteristics like popular items and choices.
5. Build the clustering model by implementing the Apriori algorithm.
6. Run the model (make predictions).
7. Display clustering results (quantitative and visual).
8. Explain the meaning of each step in the context of the dataset.
9. Interpret results and adjust your clustering.
10. Validate the model, addressing support, confidence, lift, and conviction. Then, explain the results.

Prepare a comprehensive technical report as Jupyter notebook, including all code, code comments, all outputs, plots, and analysis. Make sure the project documentation contains:

a) Problem statement

b) Algorithm of the solution

c) Analysis of the findings

d) References