Sparse Data

Transactional data is typically sparse, with missing items indicating absence rather than null values.

Missing items in a collection indicate sparsity. Missing items may be present with a null value, or they may be missing.

Nulls in transactional data are assumed to represent values that are known but not present in the transaction. For example, three items out of hundreds of possible items might be purchased in a single transaction. The items that were not purchased are known but not present in the transaction.

Oracle Machine Learning assumes sparsity in transactional data. The Apriori algorithm is optimized for processing sparse data.

Note:

Apriori is not affected by Automatic Data Preparation.