Contents
How do you classify association rules?
Classification using association rules combines association rule mining and classification, and is therefore concerned with finding rules that accurately predict a single target (class) variable. The key strength of association rule mining is that all interesting rules are found.
What is an association model?
The Association Rule model represents rules where some set of items is associated to another set of items. For example a rule can express that a certain product or set of products is often bought in combination with a certain set of other products, also known as Market Basket Analysis.
What do the rules of Association look like?
Lets now see what an association rule exactly looks like. It consists of an antecedent and a consequent, both of which are a list of items. Note that implication here is co-occurrence and not causality. For a given rule, itemset is the list of all the items in the antecedent and the consequent.
How are association rules used in data mining?
Association rules analysis is a technique to uncover how items are associated to each other. There are three common ways to measure association. Measure 1: Support. This says how popular an itemset is, as measured by the proportion of transactions in which an itemset appears.
Which is the best way to measure association?
There are three common ways to measure association. Measure 1: Support. This says how popular an itemset is, as measured by the proportion of transactions in which an itemset appears. In Table 1 below, the support of {apple} is 4 out of 8, or 50%. Itemsets can also contain multiple items.
When does an itemet have an association rule?
An itemset is frequent when its support is greater than, or equal to, a minimum support threshold MinSup. Association rules identify collections of itemsets (ie, set of features) that are statistically related (ie, frequent) in the underlying dataset.