The Frequent Pattern Mining Processor finds equal patterns within given input columns.
Frequent Pattern Mining (AKA Association Rule Mining) is an analytical process that finds frequent patterns, associations, or causal structures from datasets found in various kinds of databases such as relational databases, transactional databases, and other data repositories. Given a set of transactions, this process aims to find the rules that enable us to predict the occurrence of a specific item based on the occurrence of other items in the transaction.
The input dataset should contain one or more columns with redundant patterns.
The following link contains more information about the confidence and the support of the rule x->y.
The output table can be viewed under the result tab within the processor. It contains three columns:
- Antecedent: x in the rule x->y, here it can be either a pair or a sequence of pairs.
- Consequent: y in the rule x->y, here it can be either a pair or a sequence of pairs.
- Confidence: The occurrence of (x, y) divided by the occurrence of x in the rule x->y.