Association Rule Mining

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# Association Rule Mining - PowerPoint PPT Presentation

Association Rule Mining. The Task. Two ways of defining the task General Input: A collection of instances Output: rules to predict the values of any attribute(s) (not just the class attribute) from values of other attributes E.g. if temperature = cool then humidity =normal

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### Association Rule Mining

• Two ways of defining the task
• General
• Input: A collection of instances
• Output: rules to predict the values of any attribute(s) (not just the class attribute) from values of other attributes
• E.g. if temperature = cool then humidity =normal
• If the right hand side of a rule has only the class attribute, then the rule is a classification rule
• Distinction: Classification rules are applied together as sets of rules
• Input: a collection of transactions
• Output: rules to predict the occurrence of any item(s) from the occurrence of other items in a transaction
• E.g. {Milk, Diaper} -> {Beer}
• General rule structure:
• Antecedents -> Consequents
Association Rule Mining
• Given a set of transactions, find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction

Example of Association Rules

Implication means co-occurrence, not causality!

Definition: Frequent Itemset
• Itemset
• A collection of one or more items
• k-itemset
• An itemset that contains k items
• Support count ()
• Frequency of occurrence of an itemset
• E.g. ({Milk, Bread,Diaper}) = 2
• Support
• Fraction of transactions that contain an itemset
• E.g. s({Milk, Bread, Diaper}) = 2/5
• Frequent Itemset
• An itemset whose support is greater than or equal to a minsup threshold

Example:

Definition: Association Rule
• Association Rule
• An implication expression of the form X  Y, where X and Y are itemsets
• Example: {Milk, Diaper}  {Beer}
• Rule Evaluation Metrics
• Support (s)
• Fraction of transactions that contain both X and Y
• Confidence (c)
• Measures how often items in Y appear in transactions thatcontain X
• Given a set of transactions T, the goal of association rule mining is to find all rules having
• support ≥ minsup threshold
• confidence ≥ minconf threshold
• Brute-force approach:
• List all possible association rules
• Compute the support and confidence for each rule
• Prune rules that fail the minsup and minconf thresholds

 Computationally prohibitive!

Mining Association Rules

Example of Rules:

{Milk,Diaper}  {Beer} (s=0.4, c=0.67){Milk,Beer}  {Diaper} (s=0.4, c=1.0)

{Diaper,Beer}  {Milk} (s=0.4, c=0.67)

{Beer}  {Milk,Diaper} (s=0.4, c=0.67) {Diaper}  {Milk,Beer} (s=0.4, c=0.5)

{Milk}  {Diaper,Beer} (s=0.4, c=0.5)

• Observations:
• All the above rules are binary partitions of the same itemset: {Milk, Diaper, Beer}
• Rules originating from the same itemset have identical support but can have different confidence
• Thus, we may decouple the support and confidence requirements
Mining Association Rules
• Two-step approach:
• Frequent Itemset Generation
• Generate all itemsets whose support  minsup
• Rule Generation
• Generate high confidence rules from each frequent itemset, where each rule is a binary partitioning of a frequent itemset
• Frequent itemset generation is still computationally expensive
Power set
• Given a set S, power set, P is the set of all subsets of S
• Known property of power sets
• If S has n number of elements, P will have N = 2n number of elements.
• Examples:
• For S = {}, P={{}}, N = 20 = 1
• For S = {Milk}, P={{}, {Milk}}, N=21=2
• For S = {Milk, Diaper}
• P={{},{Milk}, {Diaper}, {Milk, Diaper}}, N=22=4
• For S = {Milk, Diaper, Beer},
• P={{},{Milk}, {Diaper}, {Beer}, {Milk, Diaper}, {Diaper, Beer}, {Beer, Milk}, {Milk, Diaper, Beer}}, N=23=8
Brute Force approach to Frequent Itemset Generation
• For an itemset with 3 elements, we have 8 subsets
• Each subset is a candidate frequent itemset which needs to be matched against each transaction

1-itemsets

2-itemsets

3-itemsets

Important Observation: Counts of subsets can’t be smaller than the count of an itemset!

Reducing Number of Candidates
• Apriori principle:
• If an itemset is frequent, then all of its subsets must also be frequent
• Apriori principle holds due to the following property of the support measure:
• Support of an itemset never exceeds the support of its subsets
• This is known as the anti-monotone property of support
Illustrating Apriori Principle

Items (1-itemsets)

Pairs (2-itemsets)

(No need to generatecandidates involving Cokeor Eggs)

Minimum Support = 3

Triplets (3-itemsets)

If every subset is considered,

6C1 + 6C2 + 6C3 = 41

With support-based pruning,

6 + 6 + 1 = 13

Write all possible 3-itemsets

and prune the list based on infrequent 2-itemsets

Apriori Algorithm
• Method:
• Let k=1
• Generate frequent itemsets of length 1
• Repeat until no new frequent itemsets are identified
• Generate length (k+1) candidate itemsets from length k frequent itemsets
• Prune candidate itemsets containing subsets of length k that are infrequent
• Count the support of each candidate by scanning the DB
• Eliminate candidates that are infrequent, leaving only those that are frequent

Note: This algorithm makes several passes over the transaction list

Rule Generation
• Given a frequent itemset L, find all non-empty subsets f  L such that f  L – f satisfies the minimum confidence requirement
• If {A,B,C,D} is a frequent itemset, candidate rules:

ABC D, ABD C, ACD B, BCD A, A BCD, B ACD, C ABD, D ABCAB CD, AC  BD, AD  BC, BC AD, BD AC, CD AB,

• If |L| = k, then there are 2k – 2 candidate association rules (ignoring L   and   L)
• Because, rules are generated from frequent itemsets, they automatically satisfy the minimum support threshold
• Rule generation should ensure production of rules that satisfy only the minimum confidence threshold
Rule Generation
• How to efficiently generate rules from frequent itemsets?
• In general, confidence does not have an anti-monotone property

c(ABC D) can be larger or smaller than c(AB D)

• But confidence of rules generated from the same itemset has an anti-monotone property
• e.g., L = {A,B,C,D}: c(ABC  D)  c(AB  CD)  c(A  BCD)
• Confidence is anti-monotone w.r.t. number of items on the RHS of the rule
• Example: Consider the following two rules
• {Milk}  {Diaper, Beer} has cm=0.5
• {Milk, Diaper}  {Beer} has cmd=0.67 > cm
Computing Confidence for Rules
• Unlike computing support, computing confidence does not require several passes over the transaction list
• Supports computed from frequent itemset generation can be reused
• Tables on the side show support values for all (except null) the subsets of itemset {Bread, Milk, Diaper}
• Confidence values are shown for the (23-2) = 6 rules generated from this frequent itemset

Example of Rules:

{Diaper, Bread}  {Milk} (s=0.6, c=1.0)

{Bread}  {Milk, Diaper} (s=0.6, c=0.75) {Diaper}  {Milk, Bread} (s=0.6, c=0.75)

{Milk}  {Diaper, Bread} (s=0.6, c=0.75)

For each frequent k-itemset where k > 2

Generate high confidence rules with one item in the consequent

Using these rules, iteratively generate high confidence rules with more than one items in the consequent

if any rule has low confidence then all the other rules containing the consequents can be pruned (not generated)

1-item rules

2-item rules

Rule generation in Apriori Algorithm
Evaluation
• Support and confidence used by Apriori allow a lot of rules which are not necessarily interesting
• Two options to extract interesting rules
• Using subjective knowledge
• Using objective measures (measures better than confidence)
• Subjective approaches
• Visualization – users allowed to interactively verify the discovered rules
• Template-based approach – filter out rules that do not fit the user specified templates
• Subjective interestingness measure – filter out rules that are obvious (bread -> butter) and that are non-actionable (do not lead to profits)

Association Rule: Tea  Coffee

• Confidence= P(Coffee|Tea) = 0.75
• but P(Coffee) = 0.9
• Although confidence is high, rule is misleading
• P(Coffee|Tea) = 0.9375
Drawback of Confidence
Objective Measures
• Confidence estimates rule quality in terms of the antecedent support but not consequent support
• As seen on the previous slide, support for consequent (P(coffee)) is higher than the rule confidence (P(coffee/Tea))
• Weka uses other objective measures
• Lift (A->B) = confidence(A->B)/support(B) = support(A->B)/(support(A)*support(B))
• Leverage (A->B) = support(A->B) – support(A)*support(B)
• Conviction(A->B) = support(A)*support(not B)/support(A->B)
• conviction inverts the lift ratio and also computes support for RHS not being true