Association

Data Mining: Concepts and Techniques Mining Association Rules in Large Databases

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Association rule mining

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What Is Association Mining? 

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Association rule mining:  Finding frequent patterns, associations, correlations, or causal structures among sets of items or objects in transaction databases, relational databases, and other information repositories. Applications:  Basket data analysis, cross-marketing, catalog design, loss-leader analysis, clustering, classification, etc. Examples.  Rule form: “Body → Ηead [support, confidence]”.  buys(x, “notebook”) → buys(x, “pen”) [0.5%,

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Market Basket Analysis -

To analysis buying habits of customers

“Which groups or sets of items are customers likely to purchase on a given trip to the store?”  Results may be used to plan marketing or advertising strategies, as well as catalog design.  Items that are frequently purchased can be placed together  Items that are frequently purchased can be placed on the opposite ends of the store, may attract customers who purchase such items to pick up other items along the way.  Retailers can plan which items to put on sale at reduced price 4

Association Rule: Basic Concepts 

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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  E.g., 98% of people who purchase tires and auto accessories also get automotive services done Applications  * ⇒ Maintenance Agreement (What the store should do to boost Maintenance Agreement sales)  Home Electronics ⇒ * (What other products should the store stocks up?)  Attached mailing in direct marketing

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Rule Measures: Support and Confidence Customer buys both

Find all the rules X & Y ⇒ Z with minimum confidence and support  support, s, probability that a transaction contains {X  Y  Z} Customer  confidence, c, conditional buys pen probability that a transaction minimum 50%, having {X  Y}support also contains Transaction ID Items Bought Let Zand minimum confidence 2000 A,B,C 50%, we have 1000 A,C  A ⇒ C (50%, 66.6%) 4000 A,D  C ⇒ A (50%, 100%) 5000 B,E,F Customer buys notebook

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Association Rule Mining: A Road Map

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Classification of Association rules Based on the types of values handled in the rule: If a rule concerns associations between the presence and absence of items, it is a Boolean association rule. example- buys(x, “SQLServer”) ^ buys(x, “DMBook”) → buys(x, “DBMiner”) [0.2%, 60%] If a rule describes associations between quantitative values for items or attributes, then it is a quantitative association rule. example- age(x, “30..39”) ^ income(x,

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Association Rule Mining: A Road Map 

Based on the dimensions of data involved in the rule: If the items or attributes in an association rule reference only one dimension, then it is a singledimensional rule. example- buys(x, “SQLServer”) → buys(x, “DBMiner”) If a rule references 2 or more dimension, such as the dimensions buys, time_of_transaction and customer_category, then it is a multidimensional association rule. example- age(x, “30..39”) ^ income(x, “42..48K”) → buys(x, “PC”) 8

Association Rule Mining: A Road Map 

Based on the levels of abstractions involved in the rule set : Some methods for association rule mining can find rules at differing levels of abstraction. example- age(X, “30…39”) → buys(X, “laptop computer”) age(X, “30…39”) → buys(X, “computer”) Items bought are referenced at different levels of abstraction (computer is a higher abstraction of laptop computer) 9

Association Rule Mining: A Road Map 

Based on various extensions to association mining: Association mining can be extended to correlation analysis where the absence or presence of correlated items can be identified. It can also be extended to mining maxpatterns (i.e, maximal frequent patterns) and frequent closed itemsets. A maxpattern is a frequent pattern, p, such that any proper superpattern of p is not frequent. A frequent closed itemset is a frequent closed itemset where an itemset c is closed if there exits no proper superset of c, c’, such that every transaction containing c also contains c’. Maxpatterns and frequent closed items can be used to substantially reduce the number of 10

Mining single-dimensional Boolean association rules from transactional databases

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Apriori Algorithm 

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Apriori is an influential algorithm for mining frequent itemsets for Boolean association rules. Algorithm uses prior knowledge of frequent itemset properties. Apriori employs an iterative approach known as level-wise search, where k –itemsets are used to explore (k+1) – itemsets. First, the set of frequent 1-itemsets is found. This set is denoted L1. L1 is used to find L2 ,the set of frequent 2itemsets, which is used to fine L3 and so on, until no more frequent k-itemsets can be found. The finding of each LK requires one full scan of the database 12

Mining Association Rules—An Example Transaction ID 2000 1000 4000 5000

Items Bought A,B,C A,C A,D B,E,F

Min. support 50% Min. confidence 50% Frequent Itemset Support {A} 75% {B} 50% {C} 50% {A,C} 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

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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 

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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. 14

The Apriori Algorithm  

Join Step: C is generated by joining L with itself Prune Step: Any (k-1)-itemset that is not frequent k

k-1

cannot be a subset of a frequent k-itemset 

Pseudo-code:

Ck: Candidate itemset of size k Lk : frequent itemset of size k L1 = {frequent items}; for (k = 1; Lk !=∅; k++) do begin Ck+1 = candidates generated from Lk; for each transaction t in database do increment the count of all candidates in Ck+1 that are contained in t

Lk+1 = candidates in Ck+1 with min_support end return ∪k Lk;

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The Apriori Algorithm — Example Database D TID 100 200 300 400

itemset sup. C1 {1} 2 {2} 3 Scan D {3} 3 {4} 1 {5} 3

Items 134 235 1235 25

L2 itemset sup

C2 itemset sup

2 2 3 2

{1 {1 {1 {2 {2 {3

C3 itemset {2 3 5}

Scan D

{1 3} {2 3} {2 5} {3 5}

2} 3} 5} 3} 5} 5}

1 2 1 2 3 2

L1 itemset sup. {1} {2} {3} {5}

2 3 3 3

C2 itemset {1 2} Scan D {1 {1 {2 {2 {3

3} 5} 3} 5} 5}

L3 itemset sup {2 3 5} 2 16

How to Generate Candidates? 

Suppose the items in Lk-1 are listed in an order

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Step 1: self-joining Lk-1 insert into Ck select p.item1, p.item2, …, p.itemk-1, q.itemk-1 from Lk-1 p, Lk-1 q where p.item1=q.item1, …, p.itemk-2=q.itemk-2, p.itemk-1 < q.itemk-1

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Step 2: pruning forall itemsets c in Ck do forall (k-1)-subsets s of c do if (s is not in Lk-1) then delete c from Ck

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How to Count Supports of Candidates? 

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Why counting supports of candidates a problem? 

The total number of candidates can be very huge

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One transaction may contain many candidates

Method: 

Candidate itemsets are stored in a hash-tree

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Leaf node of hash-tree contains a list of itemsets and counts

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Interior node contains a hash table

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Subset function: finds all the candidates

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Example of Generating Candidates 

L3={abc, abd, acd, ace, bcd}

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Self-joining: L3*L3

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abcd from abc and abd

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acde from acd and ace

Pruning: 

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acde is removed because ade is not in L3

C4={abcd} 19

Methods to Improve Apriori’s Efficiency 

Hash-based itemset counting: A k-itemset whose corresponding hashing bucket count is below the threshold cannot be frequent

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Transaction reduction: A transaction that does not contain any frequent k-itemset is useless in subsequent scans

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Partitioning: Any itemset that is potentially frequent in DB must be frequent in at least one of the partitions of DB

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Sampling: mining on a subset of given data, lower support threshold + a method to determine the completeness

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Dynamic itemset counting: add new candidate itemsets only when all of their subsets are estimated to be

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Is Apriori Fast Enough? — Performance Bottlenecks 

The core of the Apriori algorithm: 

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Use frequent (k – 1)-itemsets to generate candidate frequent k-itemsets Use database scan and pattern matching to collect counts for the candidate itemsets

The bottleneck of Apriori: candidate generation  Huge candidate sets: 

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104 frequent 1-itemset will generate 107 candidate 2itemsets To discover a frequent pattern of size 100, e.g., {a1, a2, …, a100}, one needs to generate 2100 ≈ 1030 candidates.

Multiple scans of database:

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Mining Frequent Patterns Without Candidate Generation 

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Compress a large database into a compact, Frequent-Pattern tree (FP-tree) structure 

highly condensed, but complete for frequent pattern mining

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avoid costly database scans

Develop an efficient, FP-tree-based frequent pattern mining method 

A divide-and-conquer methodology: decompose mining tasks into smaller ones

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Avoid candidate generation: sub-database test only!

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Construct FP-tree from a Transaction DB TID 100 200 300 400 500

Items bought (ordered) frequent items {f, a, c, d, g, i, m, p} {f, c, a, m, p} {a, b, c, f, l, m, o} {f, c, a, b, m} {b, f, h, j, o} {f, b} {b, c, k, s, p} {c, b, p} {a, f, c, e, l, p, m, n} {f, c, a, m, p}

Steps:

Header Table

2. Scan DB once, find frequent 1-itemset (single item pattern)

Item frequency head f 4 c 4 a 3 b 3 m 3 p 3

3. Order frequent items in frequency descending order 4. Scan DB again, construct FP-tree

min_support = 0.5 {} f:4 c:3

c:1 b:1

a:3

b:1 p:1

m:2

b:1

p:2

m:1

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Benefits of the FP-tree Structure 

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Completeness:  never breaks a long pattern of any transaction  preserves complete information for frequent pattern mining Compactness:  reduce irrelevant information—infrequent items are gone  frequency descending ordering: more frequent items are more likely to be shared  never be larger than the original database (if not count node-links and counts)  Example: For Connect-4 DB, compression ratio could be over 100

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Mining Frequent Patterns Using FP-tree 

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General idea (divide-and-conquer)  Recursively grow frequent pattern path using the FP-tree Method  For each item, construct its conditional patternbase, and then its conditional FP-tree  Repeat the process on each newly created conditional FP-tree  Until the resulting FP-tree is empty, or it contains only one path (single path will generate all the combinations of its sub-paths, each of which is a frequent pattern) 25

Major Steps to Mine FP-tree

1) Construct conditional pattern base for each node in the FP-tree 2) Construct conditional FP-tree from each conditional pattern-base 3) Recursively mine conditional FP-trees and grow frequent patterns obtained so far 

If the conditional FP-tree contains a single path, simply enumerate all the patterns 26

Step 1: From FP-tree to Conditional Pattern Base Starting at the frequent header table in the FP-tree  Traverse the FP-tree by following the link of each frequent item  Accumulate all of transformed prefix paths of that item to form a conditional pattern base Header Table {} 

Item frequency head f 4 c 4 a 3 b 3 m 3 p 3

f:4 c:3

c:1 b:1

a:3 m:2

b:1

p:2

m:1

Conditional pattern bases item

cond. pattern base

b:1

c

f:3

p:1

a

fc:3

b

fca:1, f:1, c:1

m

fca:2, fcab:1

p

fcam:2, cb:1 27

Properties of FP-tree for Conditional Pattern Base Construction 

Node-link property 

For any frequent item ai, all the possible frequent patterns that contain ai can be obtained by following ai's node-links, starting from ai's head in the FP-tree header

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Prefix path property 

To calculate the frequent patterns for a node ai in a path P, only the prefix sub-path of ai in P need to be accumulated, and its frequency count should carry the same count as node ai.

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Step 2: Construct Conditional FPtree 

For each pattern-base  Accumulate the count for each item in the base  Construct the FP-tree for the frequent items of the pattern base

Header Table Item frequency head f 4 c 4 a 3 b 3 m 3 p 3

{}

f:4 c:3

c:1 b:1

a:3

m-conditional pattern base: fca:2, fcab:1

{}

b:1 p:1



f:3 

m:2

b:1

c:3

p:2

m:1

a:3

All frequent patterns concerning m m, fm, cm, am, fcm, fam, cam, fcam

m-conditional FP-tree 29

Mining Frequent Patterns by Creating Conditional PatternBases Item Conditional pattern-base Conditional FP-tree p

{(fcam:2), (cb:1)}

{(c:3)}|p

m

{(fca:2), (fcab:1)}

{(f:3, c:3, a:3)}|m

b

{(fca:1), (f:1), (c:1)}

Empty

a

{(fc:3)}

{(f:3, c:3)}|a

c

{(f:3)}

{(f:3)}|c

f

Empty

Empty 30

Step 3: Recursively mine the conditional FP-tree {} {}

Cond. pattern base of “am”: (fc:3)

c:3

f:3

am-conditional FP-tree

c:3 a:3

f:3

{} Cond. pattern base of “cm”: (f:3)

f:3

m-conditional FP-tree

cm-conditional FP-tree

{} Cond. pattern base of “cam”: (f:3)

f:3 cam-conditional FP-tree 31

Single FP-tree Path Generation 

Suppose an FP-tree T has a single path P

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The complete set of frequent pattern of T can be generated by enumeration of all the combinations of the sub-paths of P {} f:3 c:3

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a:3

All frequent patterns concerning m m, fm, cm, am, fcm, fam, cam, fcam

m-conditional FP-tree 32

Principles of Frequent Pattern Growth 

Pattern growth property 

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Let α be a frequent itemset in DB, B be α's conditional pattern base, and β be an itemset in B. Then α ∪ β is a frequent itemset in DB iff β is frequent in B.

“abcdef ” is a frequent pattern, if and only if 

“abcde ” is a frequent pattern, and

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“f ” is frequent in the set of transactions containing “abcde ” 33

Why Is Frequent Pattern Growth Fast? 

Our performance study shows 

FP-growth is an order of magnitude faster than Apriori, and is also faster than treeprojection

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Reasoning 

No candidate generation, no candidate test

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Use compact data structure

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Eliminate repeated database scan

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Basic operation is counting and FP-tree

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FP-growth vs. Apriori: Scalability With the Support Threshold Data set T25I20D10K

100 90

D1 FP-grow th runtime D1 Apriori runtime

Run time(sec.)

80 70 60 50 40 30 20 10 0 0

0.5

1 1.5 2 Support threshold(%)

2.5

3

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FP-growth vs. Tree-Projection: Scalability with Support Threshold Data set T25I20D100K 140

D2 FP-growth

Runtime (sec.)

120

D2 TreeProjection

100 80 60 40 20 0 0

0.5

1

1.5

2

Support threshold (%) 36

Presentation of Association Rules (Table Form )

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Visualization of Association Rule Using Plane Graph

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Visualization of Association Rule Using Rule Graph

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Iceberg Queries 

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Icerberg query: Compute aggregates over one or a set of attributes only for those whose aggregate values is above certain threshold Example: select P.custID, P.itemID, sum(P.qty) from purchase P group by P.custID, P.itemID having sum(P.qty) >= 10

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Compute iceberg queries efficiently by Apriori:  First compute lower dimensions  Then compute higher dimensions only when all the lower ones are above the threshold 40

Mining multilevel association rules from transactional databases

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Multiple-Level Association Rules Food 

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Items often form hierarchy. Items at the lower level are expected to have lower support. Rules regarding itemsets at appropriate levels could be quite useful. Transaction database can be encoded based on dimensions and levels We can explore shared

bread

milk skim Fraser

TID T1 T2 T3 T4 T5

2%

wheat

white

Sunset

Items {111, 121, 211, 221} {111, 211, 222, 323} {112, 122, 221, 411} {111, 121} {111, 122, 211, 221, 413}

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Mining Multi-Level Associations 

A top_down, progressive deepening approach:  First find high-level strong rules: milk → bread [20%, 60%].

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Then find their lower-level “weaker” rules: 2% milk → wheat bread [6%, 50%].

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Variations at mining multiple-level association rules.  Level-crossed association rules: 2% milk → Wonder wheat bread  Association rules with multiple, alternative hierarchies: 2% milk → Wonder bread 43

Multi-level Association: Uniform Support vs. Reduced Support 

Uniform Support: the same minimum support for all levels  + One minimum support threshold. No need to examine itemsets containing any item whose ancestors do not have minimum support. 

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– Lower level items do not occur as frequently. If support

threshold  too high ⇒ miss low level associations  too low ⇒ generate too many high level associations Reduced Support: reduced minimum support at lower levels  There are 4 search strategies:    

Level-by-level independent Level-cross filtering by k-itemset Level-cross filtering by single item Controlled level-cross filtering by single item

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Uniform Support Multi-level mining with uniform support Level 1 min_sup = 5%

Level 2 min_sup = 5%

Milk [support = 10%]

2% Milk

Skim Milk

[support = 6%]

[support = 4%] Back 45

Reduced Support Multi-level mining with reduced support Level 1 min_sup = 5%

Level 2 min_sup = 3%

Milk [support = 10%]

2% Milk

Skim Milk

[support = 6%]

[support = 4%] Back 46

Multi-level Association: Redundancy Filtering

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Some rules may be redundant due to “ancestor” relationships between items. Example  milk ⇒ wheat bread [support = 8%, confidence = 70%]

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2% milk ⇒ wheat bread [support = 2%, confidence = 72%]

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We say the first rule is an ancestor of the second rule. A rule is redundant if its support is close to the “expected” value, based on the rule’s ancestor. 47

Multi-Level Mining: Progressive Deepening 

A top-down, progressive deepening approach:  First mine high-level frequent items: milk (15%), bread (10%)

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Then mine their lower-level “weaker” frequent itemsets:

2% milk (5%), wheat bread (4%)

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Different min_support threshold across multilevels lead to different algorithms:  If adopting the same min_support across multi-levels then toss t if any of t’s ancestors is infrequent. 

If adopting reduced min_support at lower levels then examine only those descendents whose ancestor’s support is frequent/non-negligible.

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Progressive Refinement of Data Mining Quality 

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Why progressive refinement? 

Mining operator can be expensive or cheap, fine or rough

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Trade speed with quality: step-by-step refinement.

Superset coverage property: 

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Preserve all the positive answers—allow a positive false test but not a false negative test.

Two- or multi-step mining: 

First apply rough/cheap operator (superset coverage) 49

Mining of Spatial Association Rules 

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Hierarchy of spatial relationship:  “g_close_to”: near_by, touch, intersect, contain, etc.  First search for rough relationship and then refine it. Two-step mining of spatial association:  Step 1: rough spatial computation (as a filter) 

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Using MBR or R-tree for rough estimation.

Step2: Detailed spatial algorithm (as refinement) 

Apply only to those objects which have passed the rough spatial association test (no less than min_support) 50

Mining multidimensional association rules from transactional databases and data warehouse

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Multi-Dimensional Association: Concepts 

Single-dimensional rules: buys(X, “milk”) ⇒ buys(X, “bread”)

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Multi-dimensional rules: 2 dimensions or predicates  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”)

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Categorical Attributes  finite number of possible values, no ordering among values 52

Techniques for Mining MD Associations Search for frequent k-predicate set:  Example: {age, occupation, buys} is a 3predicate set.  Techniques can be categorized by how age are treated. 1. Using static discretization of quantitative attributes  Quantitative attributes are statically discretized by using predefined concept hierarchies. 2. Quantitative association rules  Quantitative attributes are dynamically discretized into “bins”based on the distribution of the data. 3. Distance-based association rules 

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Static Discretization of Quantitative Attributes 

Discretized prior to mining using concept hierarchy.

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Numeric values are replaced by ranges.

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In relational database, finding all frequent kpredicate sets will require k or k+1 table()scans.

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Data cube is well suited for mining. (age)

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The cells of an n-dimensional cuboid correspond to the

(age, income)

(income)

(buys)

(age,buys) (income,buys)

predicate sets. 

Mining from data cubes

(age,income,buys) 54

Quantitative Association Rules Numeric attributes are dynamically discretized  Such that the confidence or compactness of the rules mined is maximized.  2-D quantitative association rules: A quan1 ∧ Aquan2 ⇒ Acat  Cluster “adjacent” association rules to form general rules using a 2-D grid.  Example:∧ income(X,”24K age(X,”30-34”) 

48K”) ⇒ buys(X,”high resolution TV”)

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ARCS (Association Rule Clustering System) How does ARCS work? 1. Binning 2. Find frequent predicateset 3. Clustering 4. Optimize

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Limitations of ARCS 

Only quantitative attributes on LHS of rules.

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Only 2 attributes on LHS. (2D limitation)

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An alternative to ARCS 

Non-grid-based

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equi-depth binning

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clustering based on a measure of partial completeness.

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“Mining Quantitative Association Rules in Large Relational Tables” by R. Srikant and R. Agrawal. 57

Mining Distance-based Association Rules 

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Binning methods do not capture the semantics of interval data Price($)

Equi-width (width $10)

Equi-depth (depth 2)

Distancebased

7 20 22 50 51 53

[0,10] [11,20] [21,30] [31,40] [41,50] [51,60]

[7,20] [22,50] [51,53]

[7,7] [20,22] [50,53]

Distance-based partitioning, more meaningful discretization considering:  density/number of points in an interval  “closeness” of points in an interval 58

Clusters and Distance Measurements 

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S[X] is a set of N tuples t1, t2, …, tN , projected on the attribute set X The diameter of S[X]:

∑ ∑ d ( S [ X ]) = 

N

N

i =1

j =1

distX (ti[ X ], tj[ X ])

N ( N − 1)

distx:distance metric, e.g. Euclidean distance or Manhattan 59

Clusters and Distance Measurements(Cont.) 

The diameter, d, assesses the density of a cluster CX , where X

d (CX ) ≤ d 0

CX ≥ s 0 

Finding clusters and distance-based rules  the density threshold, d , replaces the 0 notion of support  modified version of the BIRCH clustering algorithm 60

From association mining to correlation analysis

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Interestingness Measurements 

Objective measures Two popular measurements: ❶ support; and ❷ confidence

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Subjective measures (Silberschatz & Tuzhilin, KDD95) A rule (pattern) is interesting if ❶ it is unexpected (surprising to the user); and/or ❷ actionable (the user can do something with it) 62

Criticism to Support and Confidence 

Example 1: (Aggarwal & Yu, PODS98)  Among 5000 students  3000 play basketball  3750 eat cereal  2000 both play basket ball and eat cereal  play basketball ⇒ eat cereal [40%, 66.7%] is misleading because the overall percentage of students eating cereal is 75% which is higher than 66.7%.  play basketball ⇒ not eat cereal [20%, 33.3%] is far more accurate, although with lower support and confidence

basketball not basketball sum(row) cereal 2000 1750 3750 not cereal 1000 250 1250 sum(col.) 3000 2000 5000 63

Criticism to Support and Confidence (Cont.) 

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Example 2: X 1 1 1 1 0 0 0 0  X and Y: positively Y 1 1 0 0 0 0 0 0 correlated,  X and Z, negatively related Z 0 1 1 1 1 1 1 1  support and confidence of X=>Z dominates We need a measure of Rule Support Confidence dependent or P (correlated A∪ B) X=>Y 25% 50% corr = eventsA, B

P ( A) P ( B )



X=>Z 37.50%

75%

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Other Interestingness Measures: Interest 

Interest (correlation, lift)

P( A ∧ B) P( A) P( B )

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taking both P(A) and P(B) in consideration

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P(A^B)=P(B)*P(A), if A and B are independent events

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A and B negatively correlated, if the value is less than 1; otherwise A and B positively correlated X 1 1 1 1 0 0 0 0 Y 1 1 0 0 0 0 0 0 Z 0 1 1 1 1 1 1 1

Itemset

Support

Interest

X,Y X,Z Y,Z

25% 37.50% 12.50%

2 0.9 0.57

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Constraint-based association mining

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Constraint-Based Mining 

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Interactive, exploratory mining giga-bytes of data?  Could it be real? — Making good use of constraints! What kinds of constraints can be used in mining?  Knowledge type constraint: classification, association, etc.  Data constraint: SQL-like queries 

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Dimension/level constraints: 

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in relevance to region, price, brand, customer category.

Rule constraints 



Find product pairs sold together in Vancouver in Dec.’98.

small sales (price < $10) triggers big sales (sum > $200).

Interestingness constraints:

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Rule Constraints in Association Mining 

Two kind of rule constraints:  Rule form constraints: meta-rule guided mining. 

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Rule (content) constraint: constraint-based query optimization (Ng, et al., SIGMOD’98). 

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P(x, y) ^ Q(x, w) → takes(x, “database systems”).

sum(LHS) < 100 ^ min(LHS) > 20 ^ count(LHS) > 3 ^ sum(RHS) > 1000

1-variable vs. 2-variable constraints (Lakshmanan, et al. SIGMOD’99): 



1-var: A constraint confining only one side (L/R) of the rule, e.g., as shown above. 2-var: A constraint confining both sides (L and R). 

sum(LHS) < min(RHS) ^ max(RHS) < 5* sum(LHS)

68

Constrain-Based Association Query  

Database: (1) trans (TID, Itemset ), (2) itemInfo (Item, Type, Price) A constrained asso. query (CAQ) is in the form of {(S1, S2 )|C }, where C is a set of constraints on S1, S2 including frequency constraint A classification of (single-variable) constraints: 



 

Class constraint: S ⊂ A. e.g. S ⊂ Item Domain constraint:   



Sθ v, θ ∈ { =, ≠, <, ≤, >, ≥ }. e.g. S.Price < 100 vθ S, θ is ∈ or ∉. e.g. snacks ∉ S.Type Vθ S, or Sθ V, θ ∈ { ⊆, ⊂, ⊄, =, ≠ }  e.g. {snacks, sodas } ⊆ S.Type

Aggregation constraint: agg(S) θ v, where agg is in {min, max, sum, count, avg}, and θ ∈ { =, ≠, <, ≤, >, ≥ }. 

e.g. count(S1.Type) = 1 , avg(S2.Price) < 100

69

Constrained Association Query Optimization Problem 

Given a CAQ = { (S1, S2) | C }, the algorithm should be :

sound: It only finds frequent sets that satisfy the given constraints C  complete: All frequent sets satisfy the given constraints C are found A naïve solution:  Apply Apriori for finding all frequent sets, and then to test them for constraint satisfaction one by one. Our approach:  Comprehensive analysis of the properties of constraints and try to push them as deeply as possible inside the frequent set computation. 





70

Anti-monotone and Monotone Constraints 

A constraint Ca is anti-monotone iff. for any pattern S not satisfying Ca, none of the super-patterns of S can satisfy Ca



A constraint Cm is monotone iff. for any pattern S satisfying Cm, every superpattern of S also satisfies it 71

Succinct Constraint 





A subset of item Is is a succinct set, set if it can be expressed as σp(I) for some selection predicate p, where σ is a selection operator SP⊆2I is a succinct power set, set if there is a fixed number of succinct set I1, …, Ik ⊆I, s.t. SP can be expressed in terms of the strict power sets of I1, …, Ik using union and minus A constraint Cs is succinct provided

72

Convertible Constraint 





Suppose all items in patterns are listed in a total order R A constraint C is convertible antimonotone iff a pattern S satisfying the constraint implies that each suffix of S w.r.t. R also satisfies C A constraint C is convertible monotone iff a pattern S satisfying the constraint implies that each pattern of which S is a suffix w.r.t. R also satisfies C 73

Relationships Among Categories of Constraints

Succinctness Anti-monotonicity

Monotonicity

Convertible constraints Inconvertible constraints 74

Property of Constraints: Anti-Monotone 

Anti-monotonicity: If a set S violates the constraint, any superset of S violates the constraint.



Examples:





sum(S.Price) ≤ v is anti-monotone



sum(S.Price) ≥ v is not anti-monotone



sum(S.Price) = v is partly anti-monotone

Application: 

Push “sum(S.price) ≤ 1000” deeply into iterative frequent set computation.

75

Characterization of Anti-Monotonicity Constraints S θ v, θ ∈ { =, ≤, ≥ } v∈ S S⊇ V S⊆ V S= V min(S) ≤ v min(S) ≥ v min(S) = v max(S) ≤ v max(S) ≥ v max(S) = v count(S) ≤ v count(S) ≥ v count(S) = v sum(S) ≤ v sum(S) ≥ v sum(S) = v avg(S) θ v, θ ∈ { =, ≤, ≥ } (frequent constraint)

yes no no yes partly no yes partly yes no partly yes no partly yes no partly convertible (yes) 76

Example of Convertible Constraints: Avg(S) θ V 



Let R be the value descending order over the set of items  E.g. I={9, 8, 6, 4, 3, 1} Avg(S) ≥ v is convertible monotone w.r.t. R  If S is a suffix of S , avg(S ) ≥ avg(S) 1 1  



{8, 4, 3} is a suffix of {9, 8, 4, 3} avg({9, 8, 4, 3})=6 ≥ avg({8, 4, 3})=5

If S satisfies avg(S) ≥v, so does S1 

{8, 4, 3} satisfies constraint avg(S) ≥ 4, so does {9, 8, 4, 3}

77

Property of Constraints: Succinctness 





Succinctness:  For any set S1 and S2 satisfying C, S1 ∪ S2 satisfies C  Given A1 is the sets of size 1 satisfying C, then any set S satisfying C are based on A1 , i.e., it contains a subset belongs to A1 , Example :  sum(S.Price ) ≥ v is not succinct  min(S.Price ) ≤ v is succinct Optimization:  If C is succinct, then C is pre-counting prunable. The satisfaction of the constraint alone is not affected by the iterative support counting.

78

Characterization of Constraints by Succinctness S θ v, θ ∈ { =, ≤, ≥ } v∈ S S ⊇V S⊆ V S= V min(S) ≤ v min(S) ≥ v min(S) = v max(S) ≤ v max(S) ≥ v max(S) = v count(S) ≤ v count(S) ≥ v count(S) = v sum(S) ≤ v sum(S) ≥ v sum(S) = v avg(S) θ v, θ ∈ { =, ≤, ≥ } (frequent constraint)

Yes yes yes yes yes yes yes yes yes yes yes weakly weakly weakly no no no no (no) 79

Chapter 6: Mining Association Rules in Large Databases 

Association rule mining



Mining single-dimensional Boolean association rules from transactional databases



Mining multilevel association rules from transactional databases



Mining multidimensional association rules from transactional databases and data warehouse



From association mining to correlation analysis



Constraint-based association mining



Summary 80

Why Is the Big Pie Still There? 

More on constraint-based mining of associations  Boolean vs. quantitative associations 



From association to correlation and causal structure analysis. 



Association does not necessarily imply correlation or causal relationships

From intra-trasanction association to intertransaction associations 



Association on discrete vs. continuous data

E.g., break the barriers of transactions (Lu, et al. TOIS’99).

From association analysis to classification and clustering analysis 

E.g, clustering association rules 81