Multilevel Mining Association Rules:
- Items often form hierarchy.
- Items of the lower level are expected to have lower support.
- A common form of background knowledge as that an attribute may be generated or specialized according to a hierarchy of concepts.
- Rules which contain associations with hierarchy of concepts are called Multilevel Association Rules.
Fig: Hierarchy of concept
Support and confidence of Multilevel association rules:
- Generalizing / specializing values of attributes affects support and confidence.
- Support of rules increases from specialized to general.
- Support of rules decreases from general to specialized.
- Confidence is not affected for general or specialized.
Multidimensional Mining (MD) Association Rules:
- Single – dimension rules: It contains the single distinct predicate i.e. buys Buys(X, “milk”) = buys (X,”bread”)
- Multi-dimensional rule: It contains more than one predicate
- Inter-dimension association rule: It has no repeated predicate
- Age (X,”19-25”) ^ occupation (X, “student”) = buys (X, “coke”).
- Hybrid dimension association rules: It contains multiple occurrence of the same predicate i.e. buys Age(X, “19-25”) ^ buys (X, “popcorn”) = buys (X, “coke”)
- Categorical Attributes: This have finite number of possible values, no ordering among values. Example; brand, color.
- Quantitative Attributes: these are numeric and implicit ordering among values Example; age, income.