FUNDAMENTALS OF COMPUTER

Detailed explanation-1: -Hence the FP-growth algorithm is considered more efficient than the Apriori algorithm [3]. But, the FP growth algorithm has performance and scalability issues [3]. The FP-Tree, for very large data sets, will not fit in memory, making it difficult to process Big Data.

Detailed explanation-2: -Disadvantages of FP-Growth Algorithm FP Tree is more cumbersome and difficult to build than Apriori. It may be expensive. The algorithm may not fit in the shared memory when the database is large.

Detailed explanation-3: -FP-growth is known as one of the most efficient FPM algorithms [13]. It compresses the data of the frequent items into an FP-tree in memory and recursively mines all frequent patterns from this data structure without candidate generation requirement.

Detailed explanation-4: -FP-growth is an improved version of the Apriori Algorithm which is widely used for frequent pattern mining(AKA Association Rule Mining). It is used as an analytical process that finds frequent patterns or associations from data sets.

Detailed explanation-5: -Advantages Of FP Growth Algorithm This algorithm needs to scan the database only twice when compared to Apriori which scans the transactions for each iteration. The pairing of items is not done in this algorithm and this makes it faster. The database is stored in a compact version in memory.