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Introduction

Introduction. Business Problem. APPLICATIONS. APPROVED. BOOKED. Technique: Data Mining. 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.

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Introduction

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Presentation Transcript

  1. Introduction Business Problem APPLICATIONS APPROVED BOOKED

  2. Technique: Data Mining

  3. 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.

  4. Logical Flow: Pt. 1 Database(s) Attribute Name Instance Count Pivot Count S K A P By Attr.

  5. Logical Flow: Pt. 2 Attribute Name Instance Count Pivot Count Value/Range Pivot Count Value/Range Pivot Count Value/Range Pivot Count Gini Coefficient

  6. Nomenclature Variable vs. Attribute Quantitative Categorical Ordinal Nominal

  7. Structures: Pivot Relation Key Pivot Key 1 Y / N Key 2 Y / N Key 3 Y / N

  8. Structures: Mined Data Mining Relation Mining Variable Partition Element Partition Element Mining Variable Partition Element Partition Element . . .

  9. Structures: Narl Nodes

  10. Addendum RFC

  11. Addendum RFJC

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