Publication date: 11/10/2021

The Association Analysis platform identifies connections among groups of items in an independent event or transaction. In association analysis, an item is the basic object of interest. For example, an item could be a product, a web page, or a service. An item set is a list of one or more items.

The relationship between two item sets is defined by an association rule. An association rule consists of a condition item set and a consequent item set. Antecedents are the individual items in the condition item set. Association analysis identifies association rules, which predict that a consequent item set will be in a transaction, given that the condition item set is already in the transaction. Some association rules are stronger, and therefore more useful, than others. The following three performance measures describe the strength of an association rule:

• Support is the proportion of transactions in which an item set appears. A high value for support indicates that the item set occurs frequently.

• Confidence is the proportion of transactions that contain the consequent item set, given that the condition item set is in the transaction. Confidence measures the strength of implication, or the predictive power, of an association rule. Note that confidence in association analysis is not related to the concept of confidence intervals.

• Lift is the ratio of an association rule’s confidence to its expected confidence, assuming that the condition and consequent item sets appear in transactions independently. Lift measures how much the consequent item set depends on the presence of the condition item set. The minimum value for lift is 0.

– A lift ratio less than 1 indicates that the condition and consequent repel each other, because they occur together less frequently than one would expect by chance alone.

– A lift ratio close to 1 indicates that the consequent occurs at the same rate in transactions that contain the condition as one would expect from chance alone.

– A lift ratio greater than 1 indicates that the consequent item set has an affinity for the condition item set. The consequent item set occurs more often with the condition item set than one would expect by chance alone.

For more information about these performance measures, see Association Analysis Performance Measures.

The Association Analysis platform also enables you to perform singular value decomposition. Singular value decomposition (SVD) groups similar transactions and also groups similar items using a matrix reducing methodology that is different from what is used in association analysis. Use the SVD methodology to gain insights that complement what you learn from association analysis.

For more information about association analysis, see Hastie et al. (2009) and Shmueli et al. (2010). For more information about singular value decomposition, see Jolliffe (2002).

Want more information? Have questions? Get answers in the JMP User Community (community.jmp.com).