Apologies for posting on association rules in Private Mining of Association Rules, a term of art that might be confusing to topic map advocates, without defining it.
When we buy an item online, most retailers suggest that other buyers also purchased … some list of items. The “association” of those items together can be represented by a Boolean vector, composed of values for the presence or absence of an item. To form an association rule, such a vector is accompanied by support and confidence values.
The support value indicates the percentage of a data set where the association occurs. That is the items in question appear together.
The confidence value indicates what percentage of one value is accompanied by another.
Minimums of these values are known as minimal support threshold and minimal confidence threshold and typically appear together.
For more information on “association rules,” see Data Mining: Concepts and Techniques by Jiawei Han and Micheline Kamber, at page 229. (The publication date for the second edition in WorldCat (the link on the title) is wrong. Should be 2006.)
Supplemental Materials for Data Mining. I am checking on the status of the apparent 3rd edition so you might want to wait on buying a copy. Would make a great text for an advanced topic maps course that focused on populating a topic map.