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Data Mining Explained: A Manager's Guide to Customer-centric Business Intelligence [Pehme köide]

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  • Formaat: Paperback / softback, 416 pages, kõrgus x laius: 235x178 mm, kaal: 660 g, Illustrated
  • Ilmumisaeg: 22-Jan-2001
  • Kirjastus: Digital Press
  • ISBN-10: 1555582311
  • ISBN-13: 9781555582319
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Data Mining Explained: A Manager's Guide to Customer-centric Business IntelligenceSuurem pilt
  • Formaat: Paperback / softback, 416 pages, kõrgus x laius: 235x178 mm, kaal: 660 g, Illustrated
  • Ilmumisaeg: 22-Jan-2001
  • Kirjastus: Digital Press
  • ISBN-10: 1555582311
  • ISBN-13: 9781555582319
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781555582319.html
Märksõnad:
The first book for managers and technical professionals that teaches data mining in an accessible way and that explains how data mining drives next-generation customer relationship strategies.

Data Mining Explained helps technically-proficient managers and IT professionals use powerful data mining technologies to solve important business challenges, most importantly to identify and better serve customer needs. Written by data mining experts, Data Mining Explained describes how companies in general and those in key vertical markets can design and build effective technical marketing and sales strategies and operations using data mining.

Data Mining Explained makes vital and increasingly mainstream concepts and technologies accessible to a wide range of readers new to the topic. Readers will learn how data mining can help them find relationships and patterns, such as customer buying habits, within the huge stores of data they gather every day. Data Mining Explained helps readers understand how data mining is defining next-generation e-commerce and customer relationship management (CRM) and can revolutionize how organizations engage their customers.

Teaches an increasingly mainstream technology to managers and technical professionals
Explains how data mining unites customer relationship management (CRM) and business intelligence
Describes how to develop a data mining strategy

Muu info

Teaches an increasingly mainstream technology to managers and technical professionals Explains how data mining unites customer relationship management (CRM) and business intelligence Describes how to develop a data mining strategy
Foreword xv
Acknowledgments xix
I Why Data Mining Is Important 1(74)
What Is Customer-Centric Data Mining?
3(18)
Customer Relationship Management
4(1)
The Strategic Information Imperative
5(2)
Distilling Knowledge from Data
7(3)
Who Benefits from Data Mining
10(1)
Past Experience Can Be Used to Predict Future Events
11(1)
Data Mining Builds Customer Relationships
12(2)
Data Mining Yields Customer Knowledge
14(3)
Data Mining as Part of Your CRM Strategy Can Enhance Your Competitive Position
17(1)
Data Mining Is Not Magic
18(1)
Summary
19(2)
How Data Mining Can Enhance Your Services and Products
21(10)
Improved Sales and Service
21(3)
Customer Profiling
24(3)
Customer Interaction Center (CIC)
27(1)
Data Mining Can Help You Improve Your Products
28(2)
Summary
30(1)
Data Mining Can Solve Your Most Difficult Problems
31(26)
What Data Mining Does
32(1)
Data Mining Solves Four Problems
33(2)
Business Intelligence Problems Are Difficult
35(1)
The Data Mining Process
36(19)
Summary
55(2)
The Data Mining Process
57(18)
Discovery and Exploitation
57(2)
Ontologies as Models
59(1)
Scientific Basis
59(2)
Data Mining Methodologies
61(2)
Conventional System Development: Waterfall Process
63(2)
Data Mining: Rapid Prototyping
65(7)
A Generic Data Mining Project ``Schedule''
72(2)
Summary
74(1)
II Pillars of the Data Mining Framework 75(152)
The Information Technology of Business Intelligence
77(16)
Business Intelligence Tools
78(5)
Data Resources
83(5)
Business Intelligence Applications
88(1)
Processing Platforms
89(1)
Business Intelligence Philosophy
89(3)
Summary
92(1)
The Data in Data Mining
93(14)
Meta Data
94(1)
Representation: Quantization and Coding
94(2)
Feature Extraction and Enhancement
96(1)
Data Quality
97(3)
Relevance and Independence of Features
100(2)
Data Preparation
102(1)
Feature Selection
102(2)
Demographic and Behavioral Customer Data
104(1)
Summary
105(2)
The Mathematics of Data Mining
107(30)
Introducing Feature Space
107(4)
Moderate Statistics Apply
111(4)
Probability Distribution
115(2)
Standard Deviation and Z-score
117(3)
Z-score in Feature Space
120(3)
Feature Space Computations
123(4)
Clusters
127(2)
Making Feature Sets for Data Mining
129(5)
Synthesis of Features
134(1)
Good Features
135(2)
Data Mining Techniques: Knowledge Discovery
137(22)
Knowledge Is Connections
137(3)
Taxonomy of Knowledge Discovery Techniques
140(1)
Cluster Analysis and Auto-Clustering
141(5)
Link Analysis
146(3)
Visualization
149(10)
Data Mining Techniques: More Knowledge Discovery
159(32)
Rule Induction and Decision Trees
159(23)
Ten Rules Created from the Data Files
182(6)
Rules Created from Data File
188(2)
Rules Created from Data File
190(1)
Data Mining Techniques: Predictive Models
191(36)
Surveying Predictive Modeling Techniques
192(1)
Current Techniques Have the Power
193(5)
Mathematical Basics
198(1)
Polynomial Regression Models
199(10)
Machine Learning and Predictive Models
209(3)
Neural Networks (NNs)
212(1)
Decision Values and Decision Surfaces
212(3)
Multi-Layer Perceptrons (MLPs)
215(1)
Training a Simple Neural Network
216(8)
More Complex Decision Surfaces
224(3)
III Data Mining Management 227(40)
Common Reasons Data Mining Projects Fail
229(14)
Data Mining's Seven Deadly Sins
230(11)
Summary
241(2)
Overcoming Obstacles
243(14)
Correlated/Irrelevant Features
243(2)
Diluted Information
245(1)
Syntax and Semantics
245(2)
Population Imbalance
247(1)
Missing or Unreliable Ground-Truth
248(2)
Making Good Feature Sets from Bad Ones
250(2)
Associative Feature Selection
252(2)
Class Collisions
254(1)
Summary
255(2)
Successful Data Mining Project Management
257(10)
Project Delivery Concept
258(1)
Project Analysis
259(1)
Project Staffing
260(2)
Project Schedule
262(4)
Summary
266(1)
IV Data Mining in Vertical Industries 267(88)
Data Mining in Practice
269(6)
Data Mining in Practice
269(3)
Case Studies
272(1)
Summary
273(2)
Data Mining in Customer Service
275(8)
The Industry
275(1)
Challenges in Customer Service
275(1)
General Data Mining Applications
276(1)
Case Study: Effective Customer-Centric Marketing
276(5)
Summary
281(2)
Data Mining in Retail
283(6)
The Industry
283(1)
Challenges in Retail
283(1)
General Data Mining Applications
284(1)
Case Study: Catalog Retailer Database Marketing Program
284(3)
Summary
287(2)
Data Mining in Insurance
289(8)
The Industry
289(1)
Challenges
289(1)
General Data Mining Applications
290(1)
Case Study: Workers' Compensation Liability Prediction
291(4)
Summary
295(2)
Data Mining in Financial Services
297(10)
The Industry
297(1)
Challenges
298(1)
General Data Mining Applications
298(3)
Case Study: Direct Marketing Profiling
301(4)
Summary
305(2)
Data Mining in Health Care and Medicine
307(8)
The Industry
307(1)
Challenges
308(1)
General Data Mining Applications
309(2)
Case Study: Predicting Patient Diagnosis for PVD
311(2)
Summary
313(2)
Data Mining in Telecommunications
315(14)
The Industry
315(1)
Challenges in the Telecommunications Industry
315(1)
General Data Mining Applications
316(1)
Case Study: Modeling Direct Marketing Response for a Communication Service
316(3)
Case Study: A Predictive Model for Telecom Credit Risk
319(8)
Choosing Features for Profiling
327(1)
Summary
328(1)
Data Mining in Transportation and Logistics
329(10)
The Industry
329(1)
Challenges
329(1)
General Data Mining Applications
330(1)
Case Study: Maximizing Revenue Through Forecasting
330(5)
Case Study: Vehicle Tracking Optimization
335(3)
Summary
338(1)
Data Mining in Energy
339(10)
The Industry
339(1)
Challenges Faced by the Energy Industry
339(1)
General Data Mining Applications
339(1)
Case Study: A ``Shocking'' Problem---Hypothetical Prototype Iterations
340(6)
Case Study: Forecasting Energy Consumption
346(2)
Summary
348(1)
Data Mining in Government
349(6)
The Industry
349(1)
Challenges
349(1)
General Data Mining Applications
350(1)
Pattern Recognition Study
350(3)
Summary
353(2)
A Glossary 355(8)
B Bibliography 363(4)
C Vendor Information 367(4)
Directory Web Sites
367(1)
Vendor Listings
368(3)
D Statistics 101 371(4)
E Techniques Listed by Methodology Phase 375(4)
Problem Definition (Step 1)
375(1)
Data Evaluation (Step 2)
375(1)
Feature Extraction and Enhancement (Step 3)
376(1)
Prototyping/Model Development (Step 4)
377(1)
Model Evaluation (Step 5)
378(1)
Index 379