What are the most common algorithms used in association rule learning?

1 Apriori Algorithm

The apriori algorithm is one of the oldest and most widely used algorithms for association rule learning. It is based on the principle that if a set of items is frequent, then its subsets are also frequent. The algorithm works by generating candidate itemsets of increasing size and pruning those that do not meet a minimum support threshold. Then, it derives rules from the frequent itemsets that satisfy a minimum confidence threshold. The apriori algorithm is simple and intuitive, but it can be slow and memory-intensive when dealing with large datasets or low support values.

2 FP-Growth Algorithm

The FP-growth algorithm is a more efficient alternative to the apriori algorithm. It uses a data structure called a frequent pattern tree (FP-tree) to store the frequency information of the itemsets. The algorithm works by compressing the dataset into an FP-tree and then mining the tree recursively to find the frequent itemsets. Unlike the apriori algorithm, the FP-growth algorithm does not generate or test any candidate itemsets, which reduces the number of scans and computations. The FP-growth algorithm is faster and more scalable than the apriori algorithm, but it requires more complex data structures and algorithms.

3 Eclat Algorithm

The eclat algorithm is another variation of the apriori algorithm that uses a vertical data format instead of a horizontal one. It represents each item as a set of transactions that contain it, rather than each transaction as a set of items. The algorithm works by finding the frequent itemsets by intersecting the transaction sets of the items and counting their cardinality. Then, it derives rules from the frequent itemsets that meet a minimum confidence threshold. The eclat algorithm is faster and simpler than the apriori algorithm, but it can consume more memory and generate more redundant rules.

4 Relim Algorithm

The relim algorithm is a recursive elimination method for association rule learning. It uses a data structure called an itemset tree (IT-tree) to store the frequency information of the itemsets. The algorithm works by building an IT-tree from the dataset and then eliminating the infrequent items and their branches from the tree. Then, it derives rules from the remaining frequent itemsets that satisfy a minimum confidence threshold. The relim algorithm is similar to the FP-growth algorithm, but it uses less memory and generates fewer rules. However, it can be slower and less scalable than the FP-growth algorithm.

5 CARMA Algorithm

The CARMA algorithm is a hybrid approach that combines the advantages of the apriori and FP-growth algorithms. It uses a data structure called a CARMA-list to store the frequency and support information of the itemsets. The algorithm works by generating candidate itemsets of size one and two using the apriori method and then constructing a CARMA-list from the dataset. Then, it mines the CARMA-list using the FP-growth method to find the frequent itemsets of larger size. Then, it derives rules from the frequent itemsets that meet a minimum confidence threshold. The CARMA algorithm is faster and more memory-efficient than the apriori algorithm, but it can be more complex and less robust than the FP-growth algorithm.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?