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Explain multidimensional association rules.

Marks: 5/10 M

Year: Dec 2012,May 2013

1.In Multi dimensional association:

• Attributes can be categorical or quantitative.
• Quantitative attributes are numeric and incorporates hierarchy.
• Numeric attributes must be discretized.
• Multi dimensional association rule consists of more than one dimension :

Eg: buys(X,”IBM Laptop computer”)buys(X,”HP Inkjet Printer”)

2.Three approaches in mining multi dimensional association rules: I.Using static discretization of quantitative attributes. - Discretization is static and occurs prior to mining. - Discretized attributes are treated as categorical. - Use Apriori algorithm to find all k-frequent predicate sets(this requires k or k+1 table scans ). - Every subset of frequent predicate set must be frequent.

Eg:

• If in a data cube the 3D cuboid (age, income, buys) is frequent implies (age, income), (age, buys), (income, buys) are also frequent.
• Data cubes are well suited for mining since they make mining faster.
• The cells of an n-dimensional data cuboid correspond to the predicate cells.

II.Using dynamic discretization of quantitative attributes.

Known as mining Quantitative Association Rules.

Numeric attributes are dynamically discretized.

Eg: age(X,”20..25”) Λ income(X,”30K..41K”)buys (X,”Laptop,Computer ”)

GRID FOR TUPLES

III.Using distance based discretization with clustering.

This is dynamic discretization process that considers the distance between data points.

• It involves a two step mining process:
  • Perform clustering to find the interval of attributes involved.
  • Obtain association rules by searching for groups of clusters that occur together.
• The resultant rules may satisfy:
  • Clusters in the rule antecedent are strongly associated with clusters of rules in the consequent.
  • Clusters in the antecedent occur together.

Clusters in the consequent occur together.