Data Warehousing & Data Mining

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Data Warehousing

& Data Mining

Wolf-Tilo Balke Silviu Homoceanu

Institut für Informationssysteme Technische Universität Braunschweig http://www.ifis.cs.tu-bs.de

9. Business Intelligence 9.1 Business Intelligence Overview 9.2 Principles of Data Mining 9.3 Association Rule Mining

DW & DM – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 2

9. Business Intelligence

• What is Business Intelligence (BI)?

– The process, technologies and tools needed to turn data into information, information into knowledge and knowledge into plans that drive profitable business action

– BI comprises data warehousing, business analytic tools, and content/knowledge management

9.1 BI Overview

• Typical BI applications are – Customer segmentation

– Propensity to buy (customer disposition to buy) – Customer profitability

– Fraud detection

– Customer attrition (loss of customers)

– Channel optimization (connecting with the customer)

9.1 BI Overview

• Customer segmentation

– What market segments do my customers fall into, and what are their characteristics?

– Personalize customer relationships for higher customer satisfaction and retention

9.1 BI Overview

• Propensity to buy

– Which customers are most likely to respond to my promotion?

– Target the right customers

• Increase campaign profitability by focusing on the customers most

likely to buy

9.1 BI Overview

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• Customer profitability

– What is the lifetime profitability of my customer?

– Make individual business interaction decisions based on the overall profitability of customers

9.1 BI Overview

• Fraud detection

– How can I tell which transactions are likely to be fraudulent?

• If your wife has just proposed to increase your life insurance policy, you should probably order pizza for a while

– Quickly determine fraud and take immediate action to minimize damage

9.1 BI Overview

• Customer attrition

– Which customer is at risk of leaving?

– Prevent loss of high-value customers and let go of lower-value customers

• Channel optimization – What is the best channel to reach

my customer in each segment?

– Interact with customers based on their preference and your need to manage cost

9.1 BI Overview

• Automated decision tools

– Rule-based systems that provide a solution usually in one functional area to a specific repetitive

management problem in one industry

• E.g., automated loan approval, intelligent price setting

• Business performance management (BPM) – A framework for defining, implementing and managing

an enterprise’s business strategy by linking objectives with factual measures - key performance indicators

9.1 BI Overview

• Dashboards

– Provide a comprehensive visual view of corporate performance measures, trends, and exceptions from multiple

business areas

• Allows executives to see hot spots in seconds and explore the situation

9.1 BI Overview

• What is data mining (knowledge discovery in databases)?

– Extraction of interesting (non-trivial, implicit,

previously unknown and potentially useful)

information or patterns from data in large databases

9.2 Data Mining

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• Market analysis

– Targeted marketing/ Customer profiling

• Find clusters of “model” customers who

share the same characteristics: interest, income level, spending habits, etc.

– Determine customer purchasing patterns over time – Cross-market analysis

• Associations/co-relations between product sales

• Prediction based on the association of information – …

9.2 Applications

• Corporate analysis and risk management – Finance planning and asset evaluation

• Cash flow analysis and prediction

• Trend analysis, time series, etc.

– Resource planning

• Summarize and compare the resources and spending

– Competition

• Monitor competitors and market directions

• Group customers into classes and a class-based pricing procedure

• Set pricing strategy in a highly competitive market

9.2 Applications

• Architecture of DM systems

9.2 Data Mining

Data Warehouse

ETL Filtering

Database or data warehouse server

Data mining engine Pattern evaluation Graphical user interface

Knowledge-base

Databases

• Association (correlation and causality) – Multi-dimensional vs. single-dimensional association – age(X, “20..29”) , income(X, “20..29K”) ⟶ buys(X, “PC”)

[support = 2%, confidence = 60%]

– contains(T, “computer”) ⟶ contains(x, “software”) [1%, 75%]

• Classification and Prediction

– Finding models (functions) that describe and distinguish classes or concepts for future predictions

– Presentation: decision-tree, classification rule, neural network – Prediction: predict some unknown or missing numerical values

9.2 Data Mining Techniques

• Cluster analysis

– Class label is unknown: group data to form new classes, e.g., advertising based on client groups – Clustering based on the principle: maximizing the

intra-class similarity and minimizing the interclass similarity

• Outlier analysis

– Outlier: a data object that does not comply with the general behavior of the data

– Can be considered as noise or exception, but is quite useful in fraud detection, rare events analysis

9.2 Data Mining Techniques

• Association rule mining has the objective of finding all co-occurrence relationships (called associations), among data items

– Classical application: market basket data analysis, which aims to discover how items are purchased by customers in a supermarket

• E.g., Cheese ⟶ Wine [support = 10%, confidence = 80%]

meaning that 10% of the customers buy cheese and wine together, and 80% of customers buying cheese also buy wine

9.3 Association Rule Mining

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• Basic concepts of association rules – Let I = {i

₁

, i

₂

, …, i

_m

} be a set of items.

Let T = {t

1

, t

2

, …, t

n

} be a set of transactions where each transaction t

i

is a set of items such that t

_i

⊆ I.

– An association rule is an implication of the form:

X ⟶ Y, where X ⊂ I, Y ⊂ I and X ⋂ Y = ∅

9.3 Association Rule Mining

• Association rule mining market basket analysis example

– I – set of all items sold in a store

• E.g., i

1

= Beef, i

2

= Chicken, i

3

= Cheese, … – T – set of transactions

• The content of a customers basket

• E.g., t

1

: Beef, Chicken, Milk; t

2

: Beef, Cheese; t

3

: Cheese, Wine; t

4

: …

– An association rule might be

• Beef, Chicken ⟶ Milk, where {Beef, Chicken} is X and {Milk} is Y

9.3 Association Rule Mining

• Rules can be weak or strong – The strength of a rule is measured by its

support and confidence

– The support of a rule X ⟶ Y, is the percentage of transactions in T that contains X and Y

• Can be seen as an estimate of the probability Pr({X,Y} ⊆ t

i

)

• With n as number of transactions in T the support of the rule X ⟶ Y is:

support = |{i | {X, Y} ⊆ t

_i

}| / n

9.3 Association Rule Mining

– The confidence of a rule X ⟶ Y, is the percentage of transactions in T containing X, that contain X ∪ Y

• Can be seen as estimate of the probability Pr(Y ⊆ t

i

|X ⊆ t

i

) confidence = |{i | {X, Y} ⊆ t

i

}| / |{j | X ⊆ t

j

}|

9.3 Association Rule Mining

• How do we interpret support and confidence?

– If support is too low, the rule may just occur due to chance

• Acting on a rule with low support may not be profitable since it covers too few cases

– If confidence is too low, we cannot reliably predict Y from X

• Objective of mining association rules is to discover all associated rules in T that have support and confidence greater than a minimum threshold (minsup, minconf)!

9.3 Association Rule Mining

• Finding rules based on support and confidence thresholds

– Let minsup = 30% and minconf = 80%

– Chicken, Clothes ⟶ Milk is valid, [sup = 3/7 (42.84%), conf = 3/3 (100%)]

– Clothes ⟶ Milk, Chicken is also valid, and there are more…

9.3 Association Rule Mining

Transactions T1 Beef, Chicken, Milk

T2 Beef, Cheese

T3 Cheese, Boots

T4 Beef, Chicken, Cheese T5 Beef, Chicken, Clothes, Cheese, Milk T6 Clothes, Chicken, Milk T7 Chicken, Milk, Clothes

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• This is rather a simplistic view of shopping baskets

– Some important information is not considered e.g. the quantity of each item purchased, the price paid,…

• There are a large number of rule mining algorithms

– They use different strategies and data structures – Their resulting sets of rules are all the same

9.3 Association Rule Mining

• Approaches in association rule mining – Apriori algorithm

– Mining with multiple minimum supports – Mining class association rules

• The best known mining algorithm is the Apriori algorithm

– Step 1: find all frequent itemsets (set of items with support ≥ minsup)

– Step 2: use frequent itemsets to generate rules

9.3 Association Rule Mining

• Step 1: frequent itemset generation

– The key is the apriori property (downward closure property): any subset of a frequent itemset is also a frequent itemset

• E.g., for minsup = 30%

9.3 Apriori Algorithm: Step 1

Transactions T1 Beef, Chicken, Milk

T2 Beef, Cheese

T3 Cheese, Boots

T4 Beef, Chicken, Cheese T5 Beef, Chicken, Clothes, Cheese, Milk T6 Clothes, Chicken, Milk T7 Chicken, Milk, Clothes

Chicken, Clothes, Milk

Chicken, Clothes Chicken, Milk Clothes, Milk

Chicken Clothes Milk

• Finding frequent items

– Find all 1-item frequent itemsets; then all 2-item frequent itemsets, etc.

– In each iteration k, only consider itemsets that contain a k-1 frequent itemset

– Optimization: the algorithm assumes that items are sorted in lexicographic order

• The order is used throughout the algorithm in each itemset

• {w[1], w[2], …, w[k]} represents a k-itemset w consisting of items w[1], w[2], …, w[k], where w[1] < w[2] < … < w[k]

according to the lexicographic order

9.3 Apriori Algorithm: Step 1

– Initial step

• Find frequent itemsets of size 1: F

1

– Generalization, k ≥ 2

• C

_k

= candidates of size k: those itemsets of size k that could be frequent, given F

_k-1

• F

_k

= those itemsets that are actually frequent, F

_k

⊆ C

_k

(need to scan the database once)

9.3 Finding frequent items

– Generalization of candidates uses F

_k-1

as input and returns a superset (candidates) of the set of all frequent k-itemsets. It has two steps:

• Join step: generate all possible candidate itemsets C

_k

of length k, e.g., I

_k

= join(A

_k-1

, B

_k-1

) ⟺ A

_k-1

= {i

₁

, i

₂

, …, i

_k-2

, i

_k-1

} and B

k-1

= {i

1

, i

2

, …, i

k-2

, i’

k-1

} and i

k-1

< i’

k-1

; Then I

k

= {i

1

, i

2

, …, i

_k-2

, i

_k-1

, i’

_k-1

}

• Prune step: remove those candidates in C

k

that do not respect the downward closure property (include “k-1”

non-frequent subsets)

9.3 Apriori Algorithm: Step 1

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– Generalization e.g., F

₃

= {{1, 2, 3}, {1, 2, 4}, {1, 3, 4}, {1, 3, 5}, {2, 3, 4}}

• Try joining each 2 candidates from F

₃

9.3 Apriori Algorithm: Step 1

{1, 2, 4}

{2, 3, 4}

{1, 3, 4}

{1, 3, 5}

{1, 3, 5} {2, 3, 4}

{1, 2, 3}

{2, 3, 4}

{1, 2, 4}

{1, 3, 4}

{1, 3, 5}

{1, 2, 3, 4}

{1, 3, 4}

{2, 3, 4}

{1, 3, 5} {1, 3, 4, 5}

• After join C

4

= {{1, 2, 3, 4}, {1, 3, 4, 5}}

• Pruning:

• After pruning C

4

= {{1, 2, 3, 4}}

9.3 Apriori Algorithm: Step 1

{1, 2, 3, 4}

{2, 3, 4}

{1, 2, 3}

{1, 2, 4}

{1, 3, 4}

∈ F3 ⟹

{1, 2, 3, 4}

is a good candidate

F

3

= {{1, 2, 3}, {1, 2, 4}, {1, 3, 4}, {1, 3, 5}, {2, 3, 4}}

{1, 3, 4, 5}

{3, 4, 5}

{1, 3, 4}

{1, 3, 5}

{1, 4, 5}

{1, 3, 4, 5}

∉ F3 ⟹ Removed from C4

• Complete example: find frequent items, for minsup = 0.5

– First T scan ({item}:count)

• C

₁

: {1}:2, {2}:3, {3}:3, {4}:1, {5}:3

• F

1

: {1}:2, {2}:3, {3}:3, {5}:3;

{4} has a support of ¼ < 0.5 so it does not belong to the frequent items

• C

₂

= prune(join(F

₁

))

join : {1,2}, {1,3}, {1,5}, {2,3}, {2,5}, {3,5};

prune: C

₂

: {1,2}, {1,3}, {1,5}, {2,3}, {2,5}, {3,5}; (all items belong to F

₁

)

9.3 Apriori Algorithm: Step 1

TID Items T100 1, 3, 4 T200 2, 3, 5 T300 1, 2, 3, 5 T400 2, 5

– Second T scan

• C

2

: {1,2}:1, {1,3}:2, {1,5}:1, {2,3}:2, {2,5}:3, {3,5}:2

• F

2

: {1,3}:2, {2,3}:2, {2,5}:3, {3,5}:2

• Join: we could join {1,3} only with {1,4} or {1,5}, but they are not in F

2

. The only possible join in F

2

is {2, 3} with {2, 5}

resulting in {2, 3, 5};

• prune({2, 3, 5}): {2, 3}, {2, 5}, {3, 5} all belong to F

2

, hence, C

₃

: {2, 3, 5}

– Third T scan

• {2, 3, 5}:2, then sup({2, 3, 5}) = 50%, minsup condition is fulfilled. Then F

₃

: {2, 3, 5}

9.3 Apriori Algorithm: Step 1

TID Items T100 1, 3, 4 T200 2, 3, 5 T300 1, 2, 3, 5 T400 2, 5

• Step 2: generating rules from frequent itemsets – Frequent itemsets are not the same as association

rules

– One more step is needed to generate association rules: for each frequent itemset I, for each proper nonempty subset X of I:

• Let Y = I \ X; X ⟶ Y is an association rule if:

– Confidence(X ⟶ Y) ≥ minconf,

– Support(X ⟶ Y) := |{i | {X, Y} ⊆ t

i

}| / n = support(I) – Confidence(X ⟶ Y) := |{i | {X, Y} ⊆ t

i

}| / |{j | X ⊆ t

j

}|

= support(I) / support(X)

9.3 Apriori Algorithm: Step 2

• Rule generation example, minconf = 50%

– Suppose {2, 3, 5} is a frequent itemset, with sup=50%, as calculated in step 1

– Proper nonempty subsets: {2, 3}, {2, 5}, {3, 5}, {2}, {3}, {5}, with sup=50%, 75%, 50%, 75%, 75%, 75% respectively – These generate the following association rules:

• 2,3 ⟶ 5, confidence=100%; (sup(I)=50%; sup{2,3}=50%;

50/50= 1)

• 2,5 ⟶ 3, confidence=67%; (50/75)

• 3,5 ⟶ 2, confidence=100%; (…)

• 2 ⟶ 3,5, confidence=67%

• 3 ⟶ 2,5, confidence=67%

• 5 ⟶ 2,3, confidence=67%

– All rules have support of at least 50%

9.3 Apriori Algorithm: Step 2

TID Items T100 1, 3, 4 T200 2, 3, 5 T300 1, 2, 3, 5 T400 2, 5

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• Rule generation, summary

– In order to obtain X ⟶ Y, we need to know support(I) and support(X)

– All the required information for confidence computation has already been recorded in itemset generation

• No need to read the transactions data any more

• This step is not as time-consuming as frequent itemsets generation

9.3 Apriori Algorithm: Step 2

• Apriori Algorithm, summary

– If k is the size of the largest itemset, then it makes at most k passes over data (in practice, k is bounded e.g., 10)

– The mining exploits sparseness of data, and high minsup and minconf thresholds

– High minsup threshold makes it impossible to find rules involving rare items in the data.

The solution is a mining with multiple minimum supports approach

9.3 Apriori Algorithm

• Mining with multiple minimum supports – Single minimum support assumes that all items in the

data are of the same nature and/or have similar frequencies, which is incorrect…

– In practice, some items appear very frequently in the data, while others rarely appear

• E.g., in a supermarket, people buy cooking pans much less frequently than they buy bread and milk

9.3 Multiple Minimum Supports

• Rare item problem: if the frequencies of items vary significantly, we encounter two problems

– If minsup is set too high, those rules that involve rare items will not be found

– To find rules that involve both frequent and rare items, minsup has to be set very low. This may cause combinatorial explosion because those frequent items will be associated with one another in all possible ways

9.3 Multiple Minimum Supports

• Multiple Minimum Supports

– Each item can have a minimum item support

• Different support requirements for different rules – To prevent very frequent items and very rare items

from appearing in the same itemset S, we introduce a support difference constraint (φ)

• max

i∈S

{sup(i)} - min

i∈S

{sup(i)} ≤ φ,

where 0 ≤ φ ≤ 1 is user specified

9.3 Multiple Minimum Supports

• Minsup of a rule

– Let MIS(i) be the minimum item support (MIS) value of item i. The minsup of a rule R is the lowest MIS value of the items in the rule:

• Rule R: i

1

, i

₂

, …, i

_k

⟶ i

_k+1

, …, i

_r

satisfies its minimum support if its actual support is ≥ min(MIS(i

1

), MIS(i

2

), …, MIS(i

r

))

• E.g., the user-specified MIS values are as follows:

MIS(bread) = 2%, MIS(shoes) = 0.1%, MIS(clothes) = 0.2%

– clothes ⟶ bread [sup=0.15%,conf =70%] doesn’t satisfy its minsup – clothes ⟶ shoes [sup=0.15%,conf =70%] satisfies its minsup

9.3 Multiple Minimum Supports

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• Downward closure property is not valid anymore

– E.g., consider four items 1, 2, 3 and 4 in a database

Their minimum item supports are

• MIS(1) = 10%, MIS(2) = 20%, MIS(3) = 5%, MIS(4) = 6%

• {1, 2} has a support of 9% and it is infrequent since min(10%, 20%) > 9%, but {1, 2, 3} is with a support of 7%

frequent because min(10%, 20%, 5%) < 7%

– If applied, downward closure, eliminates {1, 2} so that {1, 2, 3} is never evaluated

9.3 Multiple Minimum Supports

• How do we solve the downward closure property problem?

– Sort all items in I according to their MIS values (make it a total order)

• The order is used throughout the algorithm in each itemset – Each itemset w is of the following form:

• {w[1], w[2], …, w[k]}, consisting of items, w[1], w[2], …, w[k], where MIS(w[1]) ≤ MIS(w[2]) ≤ … ≤ MIS(w[k])

9.3 Multiple Minimum Supports

• Multiple minimum supports is an extension of the Apriori algorithm

– Step 1: frequent itemset generation

• Initial step

– Produce the seeds for generating candidate itemsets

• Candidate generation – For k = 2

• Generalization

– For k > 2, pruning step differs from the Apriori algorithm

– Step 2: rule generation

9.3 Multiple Minimum Supports

• Step 1: frequent itemset generation

– E.g., I={1, 2, 3, 4}, with given MIS(1)=10%, MIS(2)=20%, MIS(3)=5%, MIS(4)=6%, and consider n=100

transactions:

– Initial step

• Sort I according to the MIS value of each item. Let M represent the sorted items

– Sort I, in M = {3, 4, 1, 2}

• Scan the data once to record the support count of each item

– E.g., {3}:6, {4}:3, {1}:9 and {2}:25 out of 100 transactions

9.3 Multiple Minimum Supports: Step 1

• Go through the items in M to find the first item i, that meets MIS(i). Insert it into a list of seeds L

• For each subsequent item j in M (after i), if sup(j) ≥ MIS(i), then insert j in L

–MIS(3) = 5%; sup ({3}) = 6%; sup(3) > MIS(3), so L={3}

Sup({4}) = 3% < MIS(3), so L remains {3}

Sup({1}) = 9% > MIS(3), L = {3, 1}

Sup({2}) = 25% > MIS(3), L = {3, 1, 2}

• Calculate F

₁

from L based on MIS of each item in L

–F₁ = {{3}, {2}}, since sup({1}) = 9% < MIS(1)

• Why not eliminate {1} directly? Why calculate L and not directly F?

– Downward closure property is not valid from F anymore due to multiple minimum supports

9.3 Multiple Minimum Supports: Step 1

MIS(1)=10%, MIS(2)=20%, MIS(3)=5%, MIS(4)=6%, n=100 {3}:6, {4}:3, {1}:9 {2}:25

– Candidate generation, k = 2.

Let φ = 10% (support difference)

• Take each item (seed) from L in order.

Use L and not F

₁

due to the downward closure property invalidity!

• Test the chosen item against its MIS: sup({3}) ≥ MIS(3) – If true, then we can use this value to form a level 2 candidate – If not, then go to the next element in L

• If true, e.g., sup({3}) = 6% ≥ MIS(3) = 5%, then try to form a 2 level candidate together with each of the next items in L, e.g., {3, 1}, then {3, 2}

9.3 Multiple Minimum Supports: Step 1

Items 1 2 3 4

MIS 10 20 5 6

SUP 9 25 6 3

L {3, 1, 2}

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– {3, 1} is a candidate :⟺ sup({1})≥ MIS(3) and |sup({3}) – sup({1})| ≤ φ

• sup({1}) = 9%; MIS(3) = 5%; sup({3}) = 6%; φ := 10%

9% > 5% and |6%-9%| < 10%, thus C

₂

= {3, 1}

– Now try {3, 2}

• sup({2}) = 25%; 25% > 5% but |6%-25%| > 10% so this candidate will be rejected due to the support difference constraint

9.3 Multiple Minimum Supports: Step 1

Items 1 2 3 4

MIS 10 20 5 6

SUP 9 25 6 3

L {3, 1, 2}

– Pick the next seed from L, i.e. 1 (needed to try {1,2})

• sup({1}) < MIS(1) so we can not use 1 as seed!

– Candidate generation for k=2 remains C

₂

= {{3, 1}}

• Now read the transaction list and calculate the support of each item in C

2

. Let’s assume sup({3, 1})=6, which is larger than min(MIS(3), MIS(1))

• Thus F

2

= {{3, 1}}

9.3 Multiple Minimum Supports: Step 1

Items 1 2 3 4

MIS 10 20 5 6

SUP 9 25 6 3

L {3, 1, 2}

– Generalization, k > 2 uses L

_k-1

as input and returns a superset (candidates) of the set of all frequent k- itemsets. It has two steps:

• Join step: same as in the case of k=2

I

k

= join(A

k-1

, B

k-1

) ⟺ A

k-1

= {i

1

, i

2

, …, i

k-2

, i

k-1

} and B

k-1

= {i

1

, i

2

,

…, i

_k-2

, i’

_k-1

} and i

_k-1

< i’

_k-1

and |sup(i

_k-1

) – sup(i’

_k-1

)| ≤φ.

Then I

_k

= {i

₁

, i

₂

, …, i

_k-2

, i

_k-1

, i’

_k-1

}

• Prune step: for each (k-1) subset s of I

_k

, if s is not in F

_k-1

, then I

_k

can be removed from C

_k

(it is not a good candidate).

There is however one exception to this rule, when s does not include the first item from I

_k

9.3 Multiple Minimum Supports: Step 1

– Generalization, k > 2 example: let’s consider F3={{1, 2, 3}, {1, 2, 5}, {1, 3, 4}, {1, 3, 5}, {1, 4, 5}, {1, 4, 6}, {2, 3, 5}}

• After join we obtain {1, 2, 3, 5}, {1, 3, 4, 5} and {1, 4, 5, 6} (we do not consider the support difference constraint)

• After pruning we get C

₄

= {{1, 2, 3, 5}, {1, 3, 4, 5}}

– {1, 2, 3, 5} is ok

– {1, 3, 4, 5} is not deleted although {3, 4, 5} ∉ F

3

, because MIS(3) >

MIS(1). If MIS(3) = MIS(1), it could be deleted – {1, 4, 5, 6} is deleted because {1, 5, 6} ∉ F

3

9.3 Multiple Minimum Supports: Step 1

• Step 2: rule generation

– Downward closure property is not valid anymore. As a consequence we have frequent k order items, which may contain (k-1) non-frequent sub-items

• For those non-frequent items we do not have the support value recorded

• This problem arises when we form rules of the form A,B ⟶ C, where C is the rare item:

MIS(C) = min(MIS(A), MIS(B), MIS(C))

• Conf(A,B ⟶ C) = sup({A,B,C}) / sup({A,B})

We have the frequency of {A, B, C} because it is frequent, but we don’t have the frequency to calculate support of {AB} if it is not frequent by itself

• This is called head-item problem

9.3 Multiple Minimum Supports: Step 2

• Rule generation example

– {Shoes, Clothes, Bread} is a frequent itemset since

• MIS({Shoes, Clothes, Bread}) = 0.1 < sup({Shoes, Clothes, Bread}) = 0.12

– However {Clothes, Bread} is not since neither Clothes nor Bread can seed frequent itemsets

• So we may not calculate the confidence of all rules depending on Shoes, i.e. rules:

– Clothes, Bread ⟶ Shoes – Clothes ⟶ Shoes, Bread – Bread ⟶ Shoes, Clothes

9.3 Multiple Minimum Supports: Step 2

Items Bread Clothes Shoes

MIS 2 0.2 0.1

Items {Clothes},{Bread} {Shoes, Clothes, Bread}

SUP 0.15 0.12

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• Head-item problem e.g.:

– Clothes, Bread ⟶ Shoes;

– Clothes ⟶ Shoes, Bread;

– Bread ⟶ Shoes, Clothes.

• If we have some item on the right side of a rule, which has the minimum MIS (e.g. Shoes), we may not be able to calculate the confidence without reading the data again

9.3 Multiple Minimum Supports: Step 2

• Advantages

– It is a more realistic model for practical applications – The model enables us to find rare item rules, but

without producing a huge number of meaningless rules

– By setting MIS values of some items to 100% (or more), we can exclude certain items

9.3 Multiple Minimum Supports

• Mining Class Association Rules (CAR) – Normal association rule mining doesn’t

have a target

• It finds all possible rules that exist in data, i.e., any item can appear as a consequent or a condition of a rule – However, in some applications, the user is interested

in some targets

• E.g. the user has a set of text documents from some known topics. He wants to find out what words are associated or correlated with each topic

9.3 Association Rule Mining

• CAR, example

– A text document data set

• doc 1: Student, Teach, School : Education

• doc 2: Student, School : Education

• doc 3: Teach, School, City, Game : Education

• doc 4: Baseball, Basketball : Sport

• doc 5: Basketball, Player, Spectator : Sport

• doc 6: Baseball, Coach, Game, Team : Sport

• doc 7: Basketball, Team, City, Game : Sport – Let minsup = 20% and minconf = 60%. Examples of class

association rules:

• Student, School ⟶ Education [sup= 2/7, conf = 2/2]

• Game ⟶ Sport [sup= 2/7, conf = 2/3]

9.3 Class Association Rules

• In DW, CAR can successfully be used to mine out rules for profiling the buyers of a certain product

– middle aged, has children, university degree : owns house

• CAR can also be extended with multiple minimum supports

– E.g. a data set with two classes, Yes and No. We may accept weaker positive (Yes) class rules with minimum support of 45% and only strong negative classes (No) with minimum support of 75%

– By setting minimum class supports to 100% we can skip generating rules of those classes

9.3 Class Association Rules

• Tools

– Open source projects

• Weka

• RapidMiner – Commercial

• Intelligent Miner, replaced by DB2 Data Warehouse Editions

• PASW Modeler, developed by SPSS

• Oracle Data Mining (ODM)

9.3 Association Rule Mining

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