The Sequence node discovers patterns in sequential or time-oriented data, in the format If A, then B.
The elements of a sequence include the following items:
- Item sets that constitute a single transaction. For example, if a person goes to the store and purchases bread and milk and then a few days later returns to the store and purchases some cheese, that person's buying activity can be represented as two item sets. The first item set contains bread and milk, and the second one contains cheese.
- A sequence that is a list of item sets that tend to occur in a predictable order. The Sequence node detects frequent sequences and creates a generated model node that can be used to make predictions.
The Sequence node is based on the CARMA association rules algorithm, which uses an efficient two-pass method for finding sequences.
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