Transcript Example Problem

The Marriage of Market Basket Analysis
to Predictive Modeling
Sanford Gayle
How Would You Mine This
Transactional Data?
Is Data Mining Simply
Market Basket Analysis?
Market Basket Analysis identifies the rule
/our_company/bboard/hr/café/ … but
• How do you use this information?
• Can the information be used to develop a predictive
model?
• More generally, how do you develop predictive
models using transactional tables?
Data Mining Software Objectives
•
•
•
•
Predictive Modeling
Clustering
Market Basket Analysis
Feature Discovery; that is, improve the
predictive accuracy of existing models
Agenda
• Converting a transactional to a
modeling table
• The curse of dimensionality & possible fixes
• A feature discovery process; using market
basket analysis output as an input to predictive
modeling
• A dimensional reduction scheme using
confidence
DM Table Structures
•
Transactional tables (Market Basket Analysis)
Trans-id
page
spend
count
id-1
id-1
id-1
id-1
id-1
id-2
•
page1
page2
page3
page4
page5
page1
$0
$0
$0
$19.99
$0
$0
1
1
1
1
1
1
Modeling tables (modeling & clustering tools)
Trans-id
page
spend
count
id-1
id-2
.
.
$19.95
$0
5
1
Converting Transactional Into Modeling Data
• Continuous variable case - easy
• Collapse the spend or count columns via the sum, mean, or
frequency statistic for each transaction-id value
• Proc sql;
create table new as select id,sum(amount) as total
from old
group by id;
• Categorical variable case - challenging
• It seems the detail page information is lost when the rows are
rolled-up or collapsed
• However, with transposition you collapse the rows onto a
single row for each id, with each distinct page now being a
column in the modeling table and taking the count or sum
statistic as its value
The Input Discovery Process
•
Existing modeling table contains:
id-1, age, income, job-category, married, recency,
frequency, zip-code …
•
New potential predictors per transpose contains:
id-1, spend on page1, spend on page2, spend on page3,
spend on page4, spend on page5
•
Augment existing modeling table with the new inputs
and, hopefully, discover new, significant predictors to
improve predictive accuracy
Problem with Transpose Method
•
•
•
Suppose the server has 1,000 distinct pages;
the transpose method now produces 1,000 new
columns instead of 5
Sparsity: new columns have a preponderance
of missing values; e.g., id-2 will have 5
missing values and the 1 non-missing
Regression, Neural, and Cluster tools struggle
with this many variables, especially when
there is such a preponderance of the same
values (e.g., zeros or missing)
The Curse of Dimensionality
•
•
•
Suppose interest lies in a second classification
column too; e.g., both time (hour) and page
visited
Transpose method now produces 1,000+24 new
variables, assuming no interest in interactions
If interactions are of interest, then there will be
24,000 (1,000x24) new variable generated
General Fix
• Reduce the number of levels of the
categorical variable (e.g., using confidence)
• Use the transpose method to convert the
transactional to a modeling table
• Add the new inputs to the traditional
modeling table in an effort to improve
predictive accuracy
Creating Rules-Based Dummy Variables
•
•
•
•
•
•
Obtain rules using market basket analysis
Choose the rule of interest
Identify folks having the rule of interest in their
market basket
Create a dummy variable flagging them
Augment the traditional modeling table with the
dummy variable
Use the dummy variable as an input or target in a
predictive modeling tool
Using SQL to Identify Folks Having a
Rule of Interest in Their Market Basket
Creating a Rule-Based Dummy Variable
The All-Info Table
Feature Discovery:
A new potential predictor or input
Possible Sub-setting Criteria
•
•
•
•
Any rule of interest
The confidence - e.g., all rules having confidence
>= 100 (optimal level of confidence?)
The support - e.g., all rules having support >= 10
(optimal level of support?)
The lift - e.g., all rules having lift >= 5 (optimal
level of lift)
Using Confidence as the Basis for a
Reclassification Scheme
•
•
•
Suppose diapersbeer has a confidence of
100%
Then the two levels “diapers” & “beer” can be
mapped into the value “diapersbeer”, it
seems
Actually, both the rule and its reverse must
have a confidence of 100%
The Confidence Reclassification Scheme
•
•
•
If confidence for the rule and its opposite is
>80, then combine the two levels into the rulebased level
e.g., “page1” & “page2” both mapped into
“page1page2”
Using 80 instead of 100 will introduce
inaccuracy, but the analyst overwhelmed with
too many levels will likely be willing to
substitute a little accuracy for dimensional
reduction
The Confidence Reclassification Scheme
•
•
•
Use the transpose method to generate candidate
predictors
Augment the traditional modeling table with
the new candidate predictors table
Develop an enhanced model using some of the
candidate predictors in the hope of fostering
predictive accuracy
Contact Information
[email protected]