HANA Apriori

Properties that can be configured for the HANA Apriori algorithm.

Syntax

Use this algorithm to find frequent itemsets patterns in large transactional datasets for generating association rules. This algorithm is used to understand what products and services customers tend to purchase at the same time. By analyzing the purchasing trends of customers with association analysis, you can predict their future behavior.

For example, the information that a customer who buys shoes is more likely to buy socks at the same time can be represented in an association rule (with a given minimum support and minimum confidence) as: Shoes=> Socks [support = 0.5, confidence= 0.1]

Note Creating models using the HANA Apriori algorithm is not supported.
HANA Apriori Properties
Table 1: Algorithm Properties
Property Description
Apriori Type Choose Apriori.
Item Column Select the columns containing the items to which you want to apply the algorithm.
TransactionID Column Select the column containing the transaction IDs to which you want to apply the algorithm.
Missing Values Select the method for handling missing values.
Possible values:
  • Ignore: The algorithm skips the records containing missing values in the independent or dependent columns.
  • Keep: The algorithm retains missing values for processing.
Support Enter a value for the minimum support of an item. The default value is 0.1.
Confidence Enter a value for the minimum confidence of rules/association. The default value is 0.8.
Maximum Item Count Enter the length of leading items and dependent items in the output. The default value is 5.
Number of Threads Enter the number of threads using which the algorithm should execute. The default value is 1.