|
|
|
|
| LEADER |
00000cam a2200000Mu 4500 |
| 001 |
EBOOKCENTRAL_on1347027954 |
| 003 |
OCoLC |
| 005 |
20240329122006.0 |
| 006 |
m o d |
| 007 |
cr cnu|||||||| |
| 008 |
230209s2015 xx o ||| 0 eng d |
| 040 |
|
|
|a EBLCP
|b eng
|c EBLCP
|d OCLCQ
|d OCLCO
|d EBLCP
|d OCLCQ
|d OCLCL
|d OCLCQ
|
| 020 |
|
|
|a 9781118868676
|
| 020 |
|
|
|a 1118868676
|
| 035 |
|
|
|a (OCoLC)1347027954
|
| 082 |
0 |
4 |
|a 006.3/12
|q OCoLC
|
| 049 |
|
|
|a UAMI
|
| 100 |
1 |
|
|a Larose, Daniel T.
|
| 245 |
1 |
0 |
|a Data Mining and Predictive Analytics
|h [electronic resource].
|
| 260 |
|
|
|a Newark :
|b John Wiley & Sons, Incorporated,
|c 2015.
|
| 300 |
|
|
|a 1 online resource (827 p.).
|
| 490 |
1 |
|
|a New York Academy of Sciences Ser.
|
| 500 |
|
|
|a Description based upon print version of record.
|
| 505 |
0 |
|
|a Cover -- Contents -- Preface -- Acknowledgments -- Part I Data Preparation -- Chapter 1 An Introduction to Data Mining and Predictive Analytics -- 1.1 What is Data Mining? What is Predictive Analytics? -- 1.2 Wanted: Data Miners -- 1.3 The Need for Human Direction of Data Mining -- 1.4 The Cross-Industry Standard Process for Data Mining: CRISP-DM -- 1.4.1 CRISP-DM: The Six Phases -- 1.5 Fallacies of Data Mining -- 1.6 What Tasks Can Data Mining Accomplish -- 1.6.1 Description -- 1.6.2 Estimation -- 1.6.3 Prediction -- 1.6.4 Classification -- 1.6.5 Clustering -- 1.6.6 Association -- The R Zone
|
| 505 |
8 |
|
|a R References -- Exercises -- Chapter 2 Data Preprocessing -- 2.1 Why do We Need to Preprocess the Data? -- 2.2 Data Cleaning -- 2.3 Handling Missing Data -- 2.4 Identifying Misclassifications -- 2.5 Graphical Methods for Identifying Outliers -- 2.6 Measures of Center and Spread -- 2.7 Data Transformation -- 2.8 Min-Max Normalization -- 2.9 Z-Score Standardization -- 2.10 Decimal Scaling -- 2.11 Transformations to Achieve Normality -- 2.12 Numerical Methods for Identifying Outliers -- 2.13 Flag Variables -- 2.14 Transforming Categorical Variables into Numerical Variables
|
| 505 |
8 |
|
|a 2.15 Binning Numerical Variables -- 2.16 Reclassifying Categorical Variables -- 2.17 Adding an Index Field -- 2.18 Removing Variables that are not Useful -- 2.19 Variables that Should Probably not be Removed -- 2.20 Removal of Duplicate Records -- 2.21 A Word About ID Fields -- The R Zone -- R Reference -- Exercises -- Chapter 3 Exploratory Data Analysis -- 3.1 Hypothesis Testing Versus Exploratory Data Analysis -- 3.2 Getting to Know the Data Set -- 3.3 Exploring Categorical Variables -- 3.4 Exploring Numeric Variables -- 3.5 Exploring Multivariate Relationships
|
| 505 |
8 |
|
|a 3.6 Selecting Interesting Subsets of the Data for Further Investigation -- 3.7 Using EDA to Uncover Anomalous Fields -- 3.8 Binning Based on Predictive Value -- 3.9 Deriving New Variables: Flag Variables -- 3.10 Deriving New Variables: Numerical Variables -- 3.11 Using EDA to Investigate Correlated Predictor Variables -- 3.12 Summary of Our EDA -- The R Zone -- R References -- Exercises -- Chapter 4 Dimension-Reduction Methods -- 4.1 Need for Dimension-Reduction in Data Mining -- 4.2 Principal Components Analysis -- 4.3 Applying PCA to the Houses Data Set
|
| 505 |
8 |
|
|a 4.4 How Many Components Should We Extract? -- 4.4.1 The Eigenvalue Criterion -- 4.4.2 The Proportion of Variance Explained Criterion -- 4.4.3 The Minimum Communality Criterion -- 4.4.4 The Scree Plot Criterion -- 4.5 Profiling the Principal Components -- 4.6 Communalities -- 4.6.1 Minimum Communality Criterion -- 4.7 Validation of the Principal Components -- 4.8 Factor Analysis -- 4.9 Applying Factor Analysis to the Adult Data Set -- 4.10 Factor Rotation -- 4.11 User-Defined Composites -- 4.12 An Example of a User-Defined Composite -- The R Zone -- R References -- Exercises
|
| 500 |
|
|
|a Part II Statistical Analysis
|
| 590 |
|
|
|a ProQuest Ebook Central
|b Ebook Central Academic Complete
|
| 655 |
|
0 |
|a Electronic books.
|
| 758 |
|
|
|i has work:
|a Data mining and predictive analytics (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGcY4896hY9DXvVRBHWXcX
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|a Larose, Daniel T.
|t Data Mining and Predictive Analytics
|d Newark : John Wiley & Sons, Incorporated,c2015
|z 9781118116197
|
| 830 |
|
0 |
|a New York Academy of Sciences Ser.
|
| 856 |
4 |
0 |
|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=7104155
|z Texto completo
|
| 938 |
|
|
|a ProQuest Ebook Central
|b EBLB
|n EBL7104155
|
| 994 |
|
|
|a 92
|b IZTAP
|
