What are the most important Association Rule Learning methods in Machine Learning?


Association Rule Learning is an unsupervised learning where interesting association between input data are discovered.

Here we include a brief summary of important association rule learning methods.

Apriori Algorithm

Apriori algorithm uses a bottom up approach to detect the most frequent set of items where elements are added successively as long as there is sufficient frequency. The support is computed by scanning the whole dataset for each candidate.

Equivalence Class Transformation Algorithm (Eclat)

Eclat stores a list of transactions containing the itemset and arrives at the support of any k itemset by intersecting the support of k-1 itemsets.

Frequent Pattern (FP) Growth Algorithm

FP algorithm constructs a frequent pattern tree which contains essential information on the frequent patterns followed by a divide and conquer approach to identify the most frequest itemsets.