FP growth is a scalable technique for mining frequent patterns in databases, offering significant improvements over the Apriori method. It operates through a two-step process: building an FP-tree with two passes over the dataset, and then directly extracting frequent item sets from the tree. A conditional pattern base and conditional FP tree are utilized to find frequent patterns, with results compared against Apriori's runtime performance.