Mumbai University > Information Technology > Sem6 > Data Mining and Business Intelligence

Marks: May 2015

Year: 10M

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.