Mining Association Rules between Sets of Items in Large Databases. presented by Zhuang Wang. Outline. Introduction Formal Model Apriori Algorithm Experiments Summary. Introduction. Association rule:
presented by Zhuang Wang
- Association rules are used to discover elements that co-occur frequently within a dataset consisting of multiple independent selections of elements (such as purchasing transactions), and to discover rules.
- Questions such as "if a customer purchases product A, how likely is he to purchase product B?" and "What products will a customer buy if he buys products C and D?" are answered by association-finding algorithms.(market basket analysis)
Let T be a database of transactions.
Each transaction t in T is represented as a subset of I .
Let X be a subset of I.
By an association rule, we mean an implication of the form X I_k, where X is a set of some items in I, and I_k is a single item in I that is not present in X.
support: probability that a transaction contains X and I_k.
confidence: conditional probability that a transaction having X also contains I_k.
P(l_k | X)
the sales data obtained from a large retailing company.
this data. Each transaction contains the department
numbers from which a customer bought an item in
algorithm finds if there is an association between
departments in the customer purchasing behavior.