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Differentiate between Apriori & FP-growth algorithm.

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Criteria Apriori Algorithm FP-growth Algorithm
Purpose Used for frequent itemset mining and association rule generation in data mining. Also used for frequent itemset mining and association rule generation in data mining.
Algorithm Type Apriori is an iterative candidate generation approach. FP-growth is a divide-and-conquer approach.
Developed by Rakesh Agrawal and Ramakrishnan Srikant in 1994. Jiawei Han in 2000.
Key Idea Uses candidate generation and pruning to find frequent itemsets. Employs a frequent pattern tree structure to discover frequent itemsets without candidate generation.
Candidate Generation Generates a large number of candidate itemsets. Avoids explicit candidate generation.
Data Scanning Scans the database multiple times, which can be computationally expensive for large datasets. Scans the database twice: once to build a frequent pattern tree and then to mine frequent itemsets.
Efficiency May be less efficient for large datasets with a high number of transactions and items. Generally more efficient and scalable, especially for large and sparse datasets.
Memory Usage Consumes more memory due to the need to store candidate itemsets. Typically uses less memory due to the compact tree structure.
Pruning Strategies Employs candidate pruning based on the Apriori principle. Utilizes tree pruning and conditional pattern bases.
Implementation Complexity More complex to implement due to candidate generation. Simpler to implement due to the tree structure.
Parallel Processing Challenging to parallelize effectively due to multiple iterations. More amenable to parallel processing, especially when mining large datasets.
Example Applications Market basket analysis, recommendation systems, and association rule mining. Market basket analysis, intrusion detection, and any application requiring frequent pattern mining.

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