Data%20Mining%20Techniques%20Association%20Rule

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Data Mining Techniques Association Rule
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What Is Association Mining?

• Association Rule Mining
• Finding frequent patterns, associations,
  correlations, or causal structures among item
  sets in transaction databases, relational
  databases, and other information repositories
• Applications
• Market basket analysis (marketing strategy items
  to put on sale at reduced prices),
  cross-marketing, catalog design, shelf space
  layout design, etc
• Examples
• Rule form Body Head Support, Confidence.
• buys(x, Computer) buys(x, Software) 2,
  60
• major(x, CS) takes(x, DB) grade(x, A)
  1, 75

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Market Basket Analysis
Typically, association rules are considered
interesting if they satisfy both a minimum
support threshold and a minimum confidence
threshold.
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Rule Measures Support and Confidence

• Let minimum support 50, and minimum confidence
  50, we have
• A ? C 50, 66.6
• C ? A 50, 100

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Support Confidence
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Association Rule Basic Concepts

• Given
• (1) database of transactions,
• (2) each transaction is a list of items
  (purchased by a customer in a visit)
• Find all rules that correlate the presence of one
  set of items with that of another set of items
• Find all the rules A ? B with minimum confidence
  and support
• support, s, P(A ? B)
• confidence, c, P(BA)

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Terminologies

• Item
• I1, I2, I3,
• A, B, C,
• Itemset
• I1, I1, I7, I2, I3, I5,
• A, A, G, B, C, E,
• 1-Itemset
• I1, I2, A,
• 2-Itemset
• I1, I7, I3, I5, A, G,

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Terminologies

• K-Itemset
• If the length of the itemset is K
• Frequent (Large) K-Itemset
• If the length of the itemset is K and the itemset
  satisfies a minimum support threshold.
• Association Rule
• If a rule satisfies both a minimum support
  threshold and a minimum confidence threshold

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Analysis

• The number of itemsets of a given cardinality
  tends to grow exponentially

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Fast Algorithms for Mining Association Rules
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Mining Association Rules Apriori Principle
Min. support 50 Min. confidence 50

• For rule A ? C
• support support(A ? C) 50
• confidence support(A ? C)/support(A)
  66.6
• The Apriori principle
• Any subset of a frequent itemset must be frequent

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Mining Frequent Itemsets the Key Step

• Find the frequent itemsets the sets of items
  that have minimum support
• A subset of a frequent itemset must also be a
  frequent itemset
• i.e., if AB is a frequent itemset, both A and
  B should be a frequent itemset
• Iteratively find frequent itemsets with
  cardinality from 1 to k (k-itemset)
• Use the frequent itemsets to generate
  association rules

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Example
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Example of Generating Candidates

• L3abc, abd, acd, ace, bcd
• Self-joining L3L3
• abcd from abc and abd
• acde from acd and ace
• Pruning
• acde is removed because ade is not in L3
• C4abcd

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Example
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Apriori Algorithm
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Apriori Algorithm
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Apriori Algorithm
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Exercise 4
min-sup 20 min-conf 80
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Demo-IBM Intelligent Minner
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Demo Database
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Multi-Dimensional Association

• Single-Dimensional (Intra-Dimension) Rules
  Single Dimension (Predicate) with Multiple
  Occurrences.
• buys(X, milk) ? buys(X, bread)
• Multi-Dimensional Rules ? 2 Dimensions
• Inter-dimension association rules (no repeated
  predicates)
• age(X,19-25) ? occupation(X,student) ?
  buys(X,coke)
• hybrid-dimension association rules (repeated
  predicates)
• age(X,19-25) ? buys(X, popcorn) ? buys(X,
  coke)
• Categorical (Nominal) Attributes
• finite number of possible values, no ordering
  among values
• Quantitative Attributes
• numeric, implicit ordering among values

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Exercise 5
min-sup 20 min-conf 80
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Research Topics

• Quantitative Association Rules
• buys (bread, 5) buys (milk, 3)
• Weighted Association Rules
• High Utility Association Rules
• Non-redundant Association Rule
• Constrained Association Rules Mining
• Multi-dimensional Association Rules
• Generalized Association Rules
• Negative Association Rules
• Incremental Mining Association Rules
• Data Stream Association Rule Mining
• Interactive Mining Association Rules