Data Mining II: Association Rule mining

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Data Mining II Association Rule mining
Classification

• Jagdish Gangolly
• State University of New York at Albany

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Data Mining II

• Attribute-Oriented Induction
• Mining association rules
• Mining single-dimensional boolean association
  rules
• Classification

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Attribute-Oriented Induction I

• Steps
• Original query (in DMQL)
• specify the database to be mined
• specify relevant attributes
• specify the relation to be mined
• specify the concept in the hierarchy
• Transformation of DMQL to relational query whose
  execution yields initial working relation.

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Attribute-Oriented Induction II

• Attribute removal/generalisation
• removal rule remove attribute if
• no generalisation operator on the attribute
  (large set of attribute values, but
  nogeneralisation operator)
• higher level concepts in the hierarchy expressed
  in terms of other attributes (address example)
• generalisation rule if there are many attribute
  values and there are generalisation operators,
  use them
• attribute generalisation threshold control

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Basic Algorithm for Attribute-Oriented induction

• Input Relational database, DMQL query, a list
  
• of attributes, a set of concept
  hierarchies,
• attribute generalisation
  thresholds
• Output a Prime generalised relation
• Method
• Collect task-relevant data into a working
  relation get W
• Collect statistics on the working relation
• Derive the prime relation P.

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Mining association rules I

• Some examples
• Market basket analysis analysing customer buying
  habits
• Intrusion detection by analysing user habits

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Mining association rules II

• Basic concepts
• Set of items I
• Task-relevant data D consisting of
  database transactions T ? I
• An association rule is an implication of the form
• A ? B
• where A ? I, B ? I, A ? B ?
• support(A ? B) P(A?B)
• confidence(A ? B ) P(B/A)

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Mining association rules II

• Classification of association rules
• Based on types of values
• Booleancomputer ? financial-management-software
  
• Quantitative association ruleage(X, 30..39) ?
  income(X, 42K..48K) ? buys(X,
  financial-management-software)
• Based on dimensions of data involved in the
  rulebuys(X, computer) ? buys(X,
  financial-management-software)

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Mining association rules III

• Based on levels of abstraction age(X, 30..39)
  ? buys(X, laptop) age(X, 30..39) ? buys(X,
  computer)

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Mining single-dimensional boolean association
rules I

• Apriori algorithm for finding frequent itemsets
• Apriori property (All nonempty subsets of a
  frequent itemset must also be frequent). If P(I)
  lt min_sup, then for any item A, P(I?A) lt min_sup
• Steps
• Join step A set of candidate k-itemsets, denoted
  by Ck , generated by joining Lk-1 with itself.
• Prune step Prune Ck
• Example 6-1 (p.232)

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Classification I

• Supervised learning
• Training data
• Test data
• Training data is analysed to derive
  classification rules the test data are used to
  estimate the accuracy of classification rules
• Unsupervised learning or clustering

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Classification II

• Preliminary steps
• data cleaning (reduction of noise, missing
  values, etc.)
• relevance analysis (feature selection)
• data transformation (generalisation,
  normalisation)
• Comparison/evaluation of methods
• Predictive accuracy
• speed
• Robustness
• Scalability
• Interpretability