Multilevel and Multidimensional Association rules?

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.

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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.
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