data mining association analysis stu (1)

8/10/2019 Data Mining Association Analysis Stu (1)

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ERP 345 5410

Business IntelligenceAssociation Analysis

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Source: Business Intelligence, 3rded. Sharda et.al. 2014 Prentice HallSAP University alliance, BI workshop


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Association Rule Mining

Finds interesting relationships (affinities)between variables (items or events)

Part of machine learning family Employs unsupervised learning

There is no output variable

Also known as market basket analysis Often used as an example to describe DM to

ordinary people, such as the famousrelationship between diapers and beers!


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Association Analysis

Data Mining

Cross-SellingRules

C

D

D

A

B

E

E

E

A

Customers

Products

B

C

D

What products /

services are typically

bought together?

Export rules

to Web Shop

Use in

merchandising

Association Analysis -

Example Diapers impliesBeers?

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An urban legend: Beer

Implies Diapers?? Pattern: An Analysis of behavior of

supermarket shoppers discovered that

customers who buy beer tend also todiapers??

Rationale: Men in their 20s who

purchase beer on Fridays after receivingtheir paycheck are also likely to buy apack of diapers for the young kids in the

family.4


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Association Rule Mining

Input:the simple point-of-sale transaction data

Output:Most frequent affinities among items

Example: according to the transaction dataCustomer who bought a laptop computer anda virus protection software, also boughtextended service plan 70 percent of the time"


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How to Apply Market Basket

Analysis Results? Put the items next to each other for ease of finding

Promote the items as a package

Place items far apart from each other so that thecustomer has to walk the aisles to search for it, and bydoing so potentially see and buy other items

Direct marketers can use this information to determinewhich new products to offer to their current

customers. Inventory policies can be improved if reorder points

reflect the demand for the complementary products.

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Association Rules for

Market Basket AnalysisRules are written in the form left-hand side

implies right-hand side or A=>B

Green Peppers IMPLIES BananasOranges IMPLIES Apples

Milk IMPLIES Breads

To make effective use of a rule, three numericmeasures about that rule must be considered:(1) support, (2) confidence and (3) lift

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Measures of Predictive

Ability -- Confidence Confidence measures what percentage of

baskets that contained the item A also

contained item B.Confidence of the rule: AB=

(# of transactions contain A & B)

(#of transactions contain A)

Discussion:

What does Confidence measure?8


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Ability-- Support Support refers to the percentage of baskets

where the rule was true (both items A and Bwere present).

Support of the rule: AB= (# of transactions contain A & B)

(#of all transactions)

Note: We could also define Support of any item thesame way. i.e. Support of Item A = (# oftransactions contain A) /(#of all transactions)

Questions: What is Support? Why Support?

So we would only like to retain rules with large support,9


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Ability -- Lift Lift measures how much more frequently item A

is found with item B than without item B.

Lift of the rule: AB= Confidence (of the rule: AB)

Support of B

=(# of transactions contain A & B) x (#of all transactions)(#of transactions contain A) x (#of transactions contain B)

Questions: What does lift measure? Why Lift?

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Small Example

Rule:Diapers -> Beer Support: 60% (3/5)

60% of all purchases have diapers and beer Confidence: 75% (3/4)

If diapers are purchased, 75% chance ofbuying beer

Lift: 1.25 (75%/60%) (Note: this 60% isnot the 60% of Support of the rules, butthe Support of Beer) If diapers purchased, person is 1.25 times

more likely to purchase beerurl: http://www-users.cs.umn.edu/~kumar/dmbook/ch6.pdf11
http://www-users.cs.umn.edu/~kumar/dmbook/ch6.pdfhttp://www-users.cs.umn.edu/~kumar/dmbook/ch6.pdfhttp://www-users.cs.umn.edu/~kumar/dmbook/ch6.pdfhttp://www-users.cs.umn.edu/~kumar/dmbook/ch6.pdf


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Using the Results

The tabulations can immediately betranslated into association rules and the

numerical measures computed. Comparing this weeks table to last weeks

table can immediately show the effect ofthis weeks promotional activities.

Some rules are going to be trivial. But youmay discover some interested facts/patternsfrom the data

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Limitations to Market

Basket Analysis A large number of real transactions are

needed to do an effective basket analysis,

but the datas accuracy is compromised if allthe products do not occur with similarfrequency.

The analysis can sometimes capture results

that were due to the success of previousmarketing campaigns (and not naturaltendencies of customers).

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Performing Analysis with

Virtual Items The sales data can be augmented with the

addition of virtual items. For example, we

could record that the customer was new to us,or had children.

The transaction record might look like:

Item 1: Sweater Item 2: Jacket Item 3: New

customer

This might allow us to see what patterns newcustomers have versus old customers.

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Multidimensional Market

Basket Analysis Rules can involve more than two items, for

example Plant and Clay Pot IMPLIES Soil.

These rules are built iteratively. First, pairs arefound, then relevant sets of three or four.

These are then pruned by removing those thatoccur infrequently.

In an environment like a grocery store, wherecustomers commonly buy over 100 items, rulescould involve as many as 10 items.

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Lab 5 Association Analysis

with Excel Use the transactions from slide # 11 as the input.

Develop your own excel spreadsheet (Download

the sample Excel file from Blackboard) to conductassociation analysis (with formula from slides 8, 9& 10) Report Confidence, Support and Lift for allpossible first level (A implies B) rules.

Lab Questions(1)What conclusions can you make from the

association analysis of this lab? Explain.

(2)What suggestions can you provide to the store

manager? Explain.16


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Lab 5 report

Turn in a lab report with thefollowings:

A cover page

Summary of the lab

A screenshot of your Excel table including

all the association measures of your rules.Sort your rules first by Product A, then byproduct B (Ascending orders)

Two Lab questions with your answers17

data mining association analysis stu (1)

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