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Transcript 
Introduction
Business Problem
APPLICATIONS
APPROVED
BOOKED
Technique: Data Mining
Selection by Attribute Value
Pop. Count: 1000
Pivot Count: 200
Attr. Value 'A'
Value Count: 600
Pivot Count: 150
Attr. Value 'B'
Value Count: 200
Pivot Count: 20
Attr. Value 'C'
Value Count: 200
Pivot Count: 30
Objectives
 Determine prominent characteristics of loans and/or
applicant(s) where loan is approved but not booked.
 Devise innovative and exciting ways to store
metadata using a frame-based system.
 Develop an efficient solution as measured by
database (storage) space requirements.
 Develop a solution that is generic.
Logical Flow: Pt. 1
Database(s)
S
By Attr.
K
A
P
Attribute Name
Instance Count
Pivot Count
Logical Flow: Pt. 2
Attribute Name
Instance Count
Value/Range
Pivot Count
Pivot Count
Value/Range
Pivot Count
Value/Range
Pivot Count
Gini
Coefficient
Nomenclature
Variable
Quantitative
vs.
Attribute
Categorical
Ordinal
Nominal
Structures: Pivot Relation
Key
Pivot
Key 1
Y/N
Key 2
Y/N
Key 3
Y/N
Structures: Mined Data
Mining
Relation
Mining
Variable
Partition
Element
Partition
Element
Mining
Variable
Partition
Element
Partition
Element
.
.
.
Structures: Narl Nodes
Addendum
RFC
Addendum
RFJC
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