1 Profit Mining: From Patterns to Action Ke Wang, Senqiang Zhou, Jiawei Han Simon Fraser University.

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Profit Mining:From Patterns to Action

Ke Wang, Senqiang Zhou, Jiawei Han

Simon Fraser University

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Why Profit Mining?

A major obstacle in data mining application is the gap between:– statistic-based pattern extraction and

– value-based decision making

Profit mining:– value-based data mining

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An Example Suppose we want to maximize profit. Association

rules [AIS93]

{Perfume}->Lipstick (more often)

{Perfume}->Diamond (more profit)

do not suggest which items (and prices) to

recommend to a customer who bought Perfume.

Similar problems with correlation, classification, etc.

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The Problem

Given: several transactions of form:– {<I,P,Q>,…, <I,P,Q> | <I,P,Q>}, for Item,

Promotion code, and Quantity. | separates non-target items and target items.

– {<FlakedChick., $3,2> | <Sunchip,$1,1>}

Recommend target <I,P> to customers who buy non-target items, to maximize profit.

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Not Prediction Problem

An example:– 100 customers each bought 1 pack for $1/pack.

Profit=100(1-0.5)=$50.

– 100 customers each bought 4 packs for $3.2/4-pack. Profit=100(3.2-2)=$120.

Prediction repeats the history. Profit mining gets smarter from the history, by recommending “right items” and “right prices”.

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Challenge I - notion of profit

Pure statistic approach favors– {Perfume}-> Lipstick

Pure profit approach favors– {Perfume}-> Diamond.

Profit mining considers:– both statistical significance and profit significance.

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Challenge II - customer intention

Mining On Availability (MOA):– Paying a higher price implies the willingness to

pay a lower price.

{<FC,$3>} -> <Sunchip,$1> can be extracted from transaction {<FC,$5> | <Sunchip,$1.5>}

Recognizing this behavior brings new sales opportunities (at lower price).

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Challenge III - search space

Thousands of items, and much more sales. Any combination can trigger a recommendation.

Search at alternative concepts (food, meat, etc) and prices makes it worse.

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Step 1: generating rules

Association rules – {Diaper -> Beer}, supp=10%, conf=80%

Recommendation rules:– {g1,…,gk} -> <I,P>, where gi is <Item,Price>, or Item,

or Concept.

– {<FlakedChick. , $3.8>} -> <Sunchip,$4.5>

– {FlakedChick.} -> <Sunchip,$4.5>

– {Meat} -> <Sunchip,$4.5>

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Handle alternative concept and prices

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Step 2: building the model

We rank rules by the “average profit” made by the recommendation of a rule. – {<FC,$3.5>} -> <Sunchip,$1> matches

t1: {<FC,$4.0>| <Sunchip,$2>} (a hit) t2: {<FC,$4.5>|<Milk,$3.5>} ( a miss)

– If the cost of Sunchip is $0.7, the average profit is $0.15. To recommend, we select the matching rule of the

highest possible rank.

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Step 3: Pruning the model

The model favors “high average profit” rules.

Such rules may bring a large profit. Such rules may be random noise. Cannot prune them simply based on

statistical frequency.

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Pruning the model

We prune rules to increase the estimated profit on the whole population.

We organize rules into specificity tree: the parent is the highest ranked general rule of a child.

We cut off the tree to maximize the estimated profit.

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Evaluation Synthetic datasets: IBM synthetic data generator,

modified to have price and cost. 1000 items and 1000K transactions For non-target item i:

– cost(i)=c/i

– price j=(1+j*10%)cost(i), j=1,2,3,4. For target items:

– Dataset I has 2 target items

– Dataset II has 10 target items

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Profit Gain on Dataset I

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Hit Ratio on Dataset I

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Hit Ratio on Dataset I

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Profit Gain on Dataset II

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Hit Ratio on Dataset II

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Hit Ratio on Dataset II

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Conclusion Proposed a new direction of data mining:

Mining for profit. Directly factor in business goal into data

mining Related work: microeconomic view of data

mining [KPR98]