This project (in the Midterm branch) involves creating 5 datasets and using the Apriori method to generate association rules for the 5 datasets. The apriori method finds frequent itemsets based on a minimum support and confidence threshold. The code takes in a user input for support and confidence and returns the association rules for a specified dataset
This project (in the Final branch) is broken into two parts and the coding language used to complete this project is Python. The first part involves finding and removing any outliers in a set of 500 points in a two-dimensional Euclidean space and finding the Euclidean distance between any two points. The second part implements the hierarchical agglomerative clustering algorithm on the set created from part 1 that contains no outliers in k (user-specified) clusters. This will be repeated on a total of three different datasets.