| 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. |
