|
|
|
|
| LEADER |
00000cam a2200000Mu 4500 |
| 001 |
EBOOKCENTRAL_on1347024192 |
| 003 |
OCoLC |
| 005 |
20240329122006.0 |
| 006 |
m o d |
| 007 |
cr cnu|||||||| |
| 008 |
230209s2016 xx o ||| 0 eng d |
| 040 |
|
|
|a EBLCP
|b eng
|c EBLCP
|d OCLCO
|d OCLCQ
|d EBLCP
|d OCLCQ
|
| 020 |
|
|
|a 9781118729243
|
| 020 |
|
|
|a 1118729242
|
| 035 |
|
|
|a (OCoLC)1347024192
|
| 049 |
|
|
|a UAMI
|
| 100 |
1 |
|
|a Bruce, Peter C.
|
| 245 |
1 |
0 |
|a Data Mining for Business Analytics
|h [electronic resource] :
|b Concepts, Techniques, and Applications with XLMiner.
|
| 260 |
|
|
|a Newark :
|b John Wiley & Sons, Incorporated,
|c 2016.
|
| 300 |
|
|
|a 1 online resource (601 p.).
|
| 490 |
1 |
|
|a New York Academy of Sciences Ser.
|
| 500 |
|
|
|a Description based upon print version of record.
|
| 505 |
0 |
|
|a Intro -- Title Page -- Copyright -- Dedication -- Foreword -- Preface to the Third Edition -- Preface to the First Edition -- Acknowledgments -- Part I: Preliminaries -- Chapter 1: Introduction -- 1.1 What is Business Analytics? -- 1.2 What is Data Mining? -- 1.3 Data Mining and Related Terms -- 1.4 Big Data -- 1.5 Data Science -- 1.6 Why are There so Many Different Methods? -- 1.7 Terminology and Notation -- 1.8 Road Maps to This Book -- Chapter 2: Overview of the Data Mining Process -- 2.1 Introduction -- 2.2 Core Ideas in Data Mining -- 2.3 The Steps in Data Mining -- 2.4 Preliminary Steps
|
| 505 |
8 |
|
|a 2.5 Predictive Power and Overfitting -- 2.6 Building a Predictive Model with XLMiner -- 2.7 Using Excel for Data Mining -- 2.8 Automating Data Mining Solutions -- Problems -- Part II: Data Exploration and Dimension Reduction -- Chapter 3: Data Visualization -- 3.1 Uses of Data Visualization -- 3.2 Data Examples -- 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots -- 3.4 Multidimensional Visualization -- 3.5 Specialized Visualizations -- 3.6 Summary: Major Visualizations and Operations, by Data Mining Goal -- Problems -- Chapter 4: Dimension Reduction -- 4.1 Introduction
|
| 505 |
8 |
|
|a 4.2 Curse of Dimensionality -- 4.3 Practical Considerations -- 4.4 Data Summaries -- 4.5 Correlation Analysis -- 4.6 Reducing the Number of Categories in Categorical Variables -- 4.7 Converting a Categorical Variable to a Numerical Variable -- 4.8 Principal Components Analysis -- 4.9 Dimension Reduction Using Regression Models -- 4.10 Dimension Reduction Using Classification and Regression Trees -- Problems -- Part III: Performance Evaluation -- Chapter 5: Evaluating Predictive Performance -- 5.1 Introduction -- 5.2 Evaluating Predictive Performance -- 5.3 Judging Classifier Performance
|
| 505 |
8 |
|
|a 5.4 Judging Ranking Performance -- 5.5 Oversampling -- Problems -- Part IV: Prediction and Classification Methods -- Chapter 6: Multiple Linear Regression -- 6.1 Introduction -- 6.2 Explanatory vs. Predictive Modeling -- 6.3 Estimating the Regression Equation and Prediction -- 6.4 Variable Selection in Linear Regression -- Problems -- Chapter 7: k-Nearest-Neighbors (k-NN) -- 7.1 The k-NN Classifier (Categorical Outcome) -- 7.2 k-NN for a Numerical Response -- 7.3 Advantages and Shortcomings of k-NN Algorithms -- Problems -- Chapter 8: The Naive Bayes Classifier -- 8.1 Introduction
|
| 505 |
8 |
|
|a 8.2 Applying the Full (Exact) Bayesian Classifier -- 8.3 Advantages and Shortcomings of the Naive Bayes Classifier -- Problems -- Chapter 9: Classification and Regression Trees -- 9.1 Introduction -- 9.2 Classification Trees -- 9.3 Evaluating the Performance of a Classification Tree -- 9.4 Avoiding Overfitting -- 9.5 Classification Rules from Trees -- 9.6 Classification Trees for More Than two Classes -- 9.7 Regression Trees -- 9.8 Advantages, Weaknesses, and Extensions -- 9.9 Improving Prediction: Multiple Trees -- Problems -- Chapter 10: Logistic Regression -- 10.1 Introduction
|
| 500 |
|
|
|a 10.2 The Logistic Regression Model
|
| 590 |
|
|
|a ProQuest Ebook Central
|b Ebook Central Academic Complete
|
| 655 |
|
0 |
|a Electronic books.
|
| 776 |
0 |
8 |
|i Print version:
|a Bruce, Peter C.
|t Data Mining for Business Analytics
|d Newark : John Wiley & Sons, Incorporated,c2016
|z 9781118729472
|
| 830 |
|
0 |
|a New York Academy of Sciences Ser.
|
| 856 |
4 |
0 |
|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=7104001
|z Texto completo
|
| 938 |
|
|
|a ProQuest Ebook Central
|b EBLB
|n EBL7104001
|
| 994 |
|
|
|a 92
|b IZTAP
|
