The document describes a proposed approach for efficiently mining frequent item sets from stock market data using an association rule mining technique called the Apriori algorithm. The approach involves preprocessing the data to reduce the overall mining time and using pruning techniques to eliminate item sets that do not satisfy inter-transaction criteria. It constructs an FP-tree to represent the frequent patterns in the data using two database scans, then applies an FP-Growth algorithm with pruning to discover inter-transaction association rules between stock prices and other variables. The goal is to provide deeper analysis of stock price movements over time to help financial analysts and investors.