Apriori Frequent Itemset Mining
Instance-Based, Kernel & Probabilistic Methods DS practice problem on Onlearn.
Difficulty: medium.
Topics: Understanding Frequent Itemset Generation using the Apriori Algorithm, Minimum Support Thresholding, K-itemset Join Operation, Subset Frequency Counting, Candidate Pruning Strategy, A-priori Principle, Data Mining, Association Rule Learning, Combinatorial Optimization, Set Theory, Complexity Analysis, Frequent Itemset Mining, Support and Confidence Metrics, Candidate Generation and Pruning, Transaction Database Processing, Downward Closure Property.
Implement the core logic of the Apriori algorithm to find all frequent itemsets from a transaction database. Given a list of transactions (each a set of items) and a minimum support threshold, return a dictionary where keys are the itemsets (as sorted tuples) and values are their corresponding support counts. Only include itemsets that meet or exceed the minimum support threshold.