Atjaunināt sīkdatņu piekrišanu

Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R [Mīkstie vāki]

3.60/5 (20 ratings by Goodreads)
(Simon Fraser University, Burnaby, British Columbia, Canada), (Alteryx, California, USA)
  • Formāts: Paperback / softback, 316 pages, height x width: 254x178 mm, weight: 590 g, 20 Tables, black and white; 178 Illustrations, black and white
  • Sērija : Chapman & Hall/CRC The R Series
  • Izdošanas datums: 07-May-2012
  • Izdevniecība: CRC Press Inc
  • ISBN-10: 1466503963
  • ISBN-13: 9781466503960
Citas grāmatas par šo tēmu:
  • Mīkstie vāki
  • Cena: 105,42 €
  • Grāmatu piegādes laiks ir 3-4 nedēļas, ja grāmata ir uz vietas izdevniecības noliktavā. Ja izdevējam nepieciešams publicēt jaunu tirāžu, grāmatas piegāde var aizkavēties.
  • Daudzums:
  • Ielikt grozā
  • Piegādes laiks - 4-6 nedēļas
  • Pievienot vēlmju sarakstam
Customer and Business Analytics: Applied Data Mining for Business Decision Making Using RPalielināt
  • Formāts: Paperback / softback, 316 pages, height x width: 254x178 mm, weight: 590 g, 20 Tables, black and white; 178 Illustrations, black and white
  • Sērija : Chapman & Hall/CRC The R Series
  • Izdošanas datums: 07-May-2012
  • Izdevniecība: CRC Press Inc
  • ISBN-10: 1466503963
  • ISBN-13: 9781466503960
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781466503960.html
Keywords:
Putler, with a California company, and Krider (Simon Fraser U., British Columbia) identify the kinds of business problems that advanced analytical tools can address, explain how different data mining algorithms work, and provide hands-on experience with data mining tools. They write for graduate and advanced undergraduate students of business, and for managers in small to middling companies who want to move beyond conventional database reporting. Among the topics are database marketing and data mining, basic tools for understanding data, logistic regression, tree models, and Ward's method of cluster analysis and principal components. They use R, the widely used open-source free statistical software. Annotation ©2012 Book News, Inc., Portland, OR (booknews.com)

Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. It also gives insight into some of the challenges faced when deploying these tools. Extensively classroom-tested, the text is ideal for students in customer and business analytics or applied data mining as well as professionals in small- to medium-sized organizations.

The book offers an intuitive understanding of how different analytics algorithms work. Where necessary, the authors explain the underlying mathematics in an accessible manner. Each technique presented includes a detailed tutorial that enables hands-on experience with real data. The authors also discuss issues often encountered in applied data mining projects and present the CRISP-DM process model as a practical framework for organizing these projects.

Showing how data mining can improve the performance of organizations, this book and its R-based software provide the skills and tools needed to successfully develop advanced analytics capabilities.

Recenzijas

"This book is derived from a lecture course in data mining for MBA students. assumes very little in the way of mathematical or statistical background. The writing style is generally good, and the book should prove useful to its target audience." David Scott, International Statistical Review (2013), 81, 2

List of Figures
xiii
List of Tables
xxi
Preface xxiii
I Purpose and Process
1(30)
1 Database Marketing and Data Mining
3(14)
1.1 Database Marketing
4(5)
1.1.1 Common Database Marketing Applications
5(3)
1.1.2 Obstacles to Implementing a Database Marketing Program
8(1)
1.1.3 Who Stands to Benefit the Most from the Use of Database Marketing?
9(1)
1.2 Data Mining
9(5)
1.2.1 Two Definitions of Data Mining
9(1)
1.2.2 Classes of Data Mining Methods
10(1)
1.2.2.1 Grouping Methods
10(1)
1.2.2.2 Predictive Modeling Methods
11(3)
1.3 Linking Methods to Marketing Applications
14(3)
2 A Process Model for Data Mining---CRISP-DM
17(14)
2.1 History and Background
17(2)
2.2 The Basic Structure of CRISP-DM
19(12)
2.2.1 CRISP-DM Phases
19(2)
2.2.2 The Process Model within a Phase
21(1)
2.2.3 The CRISP-DM Phases in More Detail
21(1)
2.2.3.1 Business Understanding
21(1)
2.2.3.2 Data Understanding
22(1)
2.2.3.3 Data Preparation
23(2)
2.2.3.4 Modeling
25(1)
2.2.3.5 Evaluation
26(1)
2.2.3.6 Deployment
27(1)
2.2.4 The Typical Allocation of Effort across Project Phases
28(3)
II Predictive Modeling Tools
31(202)
3 Basic Tools for Understanding Data
33(48)
3.1 Measurement Scales
34(2)
3.2 Software Tools
36(12)
3.2.1 Getting R
37(4)
3.2.2 Installing R on Windows
41(2)
3.2.3 Installing R on OS X
43(2)
3.2.4 Installing the RcmdrPlugin.BCA Package and Its Dependencies
45(3)
3.3 Reading Data into R Tutorial
48(9)
3.4 Creating Simple Summary Statistics Tutorial
57(6)
3.5 Frequency Distributions and Histograms Tutorial
63(10)
3.6 Contingency Tables Tutorial
73(8)
4 Multiple Linear Regression
81(36)
4.1 Jargon Clarification
82(1)
4.2 Graphical and Algebraic Representation of the Single Predictor Problem
83(8)
4.2.1 The Probability of a Relationship between the Variables
89(2)
4.2.2 Outliers
91(1)
4.3 Multiple Regression
91(7)
4.3.1 Categorical Predictors
92(2)
4.3.2 Nonlinear Relationships and Variable Transformations
94(3)
4.3.3 Too Many Predictor Variables: Overfitting and Adjusted R2
97(1)
4.4 Summary
98(1)
4.5 Data Visualization and Linear Regression Tutorial
99(18)
5 Logistic Regression
117(30)
5.1 A Graphical Illustration of the Problem
118(3)
5.2 The Generalized Linear Model
121(3)
5.3 Logistic Regression Details
124(2)
5.4 Logistic Regression Tutorial
126(21)
5.4.1 Highly Targeted Database Marketing
126(1)
5.4.2 Oversampling
127(1)
5.4.3 Overfitting and Model Validation
128(19)
6 Lift Charts
147(18)
6.1 Constructing Lift Charts
147(7)
6.1.1 Predict, Sort, and Compare to Actual Behavior
147(4)
6.1.2 Correcting Lift Charts for Oversampling
151(3)
6.2 Using Lift Charts
154(5)
6.3 Lift Chart Tutorial
159(6)
7 Tree Models
165(22)
7.1 The Tree Algorithm
166(6)
7.1.1 Calibrating the Tree on an Estimation Sample
167(3)
7.1.2 Stopping Rules and Controlling Overfitting
170(2)
7.2 Trees Models Tutorial
172(15)
8 Neural Network Models
187(14)
8.1 The Biological Inspiration for Artificial Neural Networks
187(5)
8.2 Artificial Neural Networks as Predictive Models
192(2)
8.3 Neural Network Models Tutorial
194(7)
9 Putting It All Together
201(32)
9.1 Stepwise Variable Selection
201(3)
9.2 The Rapid Model Development Framework
204(6)
9.2.1 Up-Selling Using the Wesbrook Database
204(1)
9.2.2 Think about the Behavior That You Are Trying to Predict
205(1)
9.2.3 Carefully Examine the Variables Contained in the Data Set
205(2)
9.2.4 Use Decision Trees and Regression to Find the Important Predictor Variables
207(1)
9.2.5 Use a Neural Network to Examine Whether Nonlinear Relationships Are Present
208(1)
9.2.6 If There Are Nonlinear Relationships, Use Visualization to Find and Understand Them
209(1)
9.3 Applying the Rapid Development Framework Tutorial
210(23)
III Grouping Methods
233(50)
10 Ward's Method of Cluster Analysis and Principal Components
235(24)
10.1 Summarizing Data Sets
235(1)
10.2 Ward's Method of Cluster Analysis
236(6)
10.2.1 A Single Variable Example
238(2)
10.2.2 Extension to Two or More Variables
240(2)
10.3 Principal Components
242(6)
10.4 Ward's Method Tutorial
248(11)
11 K-Centroids Partitioning Cluster Analysis
259(24)
11.1 How K-Centroid Clustering Works
260(4)
11.1.1 The Basic Algorithm to Find K-Centroids Clusters
260(1)
11.1.2 Specific K-Centroid Clustering Algorithms
261(3)
11.2 Cluster Types and the Nature of Customer Segments
264(3)
11.3 Methods to Assess Cluster Structure
267(8)
11.3.1 The Adjusted Rand Index to Assess Cluster Structure Reproducibility
268(6)
11.3.2 The Calinski-Harabasz Index to Assess within Cluster Homogeneity and between Cluster Separation
274(1)
11.4 K-Centroids Clustering Tutorial
275(8)
Bibliography 283(4)
Index 287
Dr. Daniel S. Putler is a Data Artisan in Residence at Alteryx, a business intelligence/analytics software company. Dr. Robert E. Krider is a professor of marketing in the Beedie School of Business at Simon Fraser University. He has also taught in Hong Kong, Shanghai, Portugal, and Germany. His research tackles questions of customer and competitor behavior in retailing and media industries.