Explain multilevel association rules with suitable examples?

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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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.

    Two Approaches of Multilevel Association Rules:

    I. Using uniform support level for all levels:

    • The same minimum support for all levels.
    • There is only one minimum support threshold so no need to examine itemsets
    • If support threshold is too high ⇒ miss low level associations
    • If the support threshold is too low ⇒ generate too many high level associations.

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Fig: Example of uniform minimum support for all levels

II. Using reduced minimum support at lower level:

  • At every level of abstraction , there is its own minimum support threshold

  • So minimum support at lower levels reduces.

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Fig: Example of reduced minimum support for lower level

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