ECU Libraries Catalog
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LEADER 04069cam 2200589 i 4500
001
ocn862096372
003
OCoLC
005
20161027124509.0
008
141114s2015 njua b 001 0 eng
010
a| 2014043340
019
a| 861734428
020
a| 9781118116197 (cloth)
020
a| 1118116194 (cloth)
029
1
a| AU@
b| 000053830362
029
1
a| NLGGC
b| 391068075
035
a| (Sirsi) o862096372
035
a| (OCoLC)862096372
z| (OCoLC)861734428
040
a| DLC
b| eng
e| rda
c| DLC
d| YDX
d| BTCTA
d| BDX
d| YDXCP
d| OCLCF
d| CDX
d| BEDGE
d| ZLM
d| UtOrBLW
042
a| pcc
049
a| EREE
050
0
0
a| QA76.9.D343
b| L3776 2015
082
0
0
a| 006.3/12
2| 23
100
1
a| Larose, Daniel T.
=| ^A872257
245
1
0
a| Data mining and predictive analytics /
c| Daniel T. Larose, Chantal D. Larose.
250
a| Second edition.
264
1
a| Hoboken, New Jersey :
b| John Wiley & Sons Inc.,
c| [2015]
300
a| xxix, 794 pages :
b| illustrations ;
c| 25 cm.
336
a| text
b| txt
2| rdacontent
337
a| unmediated
b| n
2| rdamedia
338
a| volume
b| nc
2| rdacarrier
490
1
a| Wiley series on methods and applications in data mining
504
a| Includes bibliographical references and index.
505
2
a| Part I. Data Preparation -- Chapter 1. An Introduction to Data Mining and Predictive Analytics -- Chapter 2. Data Preprocessing -- Chapter 3. Exploratory Data Analysis -- Chapter 4. Dimension-Reduction Methods -- Part II Statistical Analysis -- Chapter 5 Univariate Statistical Analysis -- Chapter 6. Multivariate Statistics -- Chapter 7. Preparing to Model the data -- Chapter 8. Simple Linear Regression -- Chapter 9. Multiple Regression and Model Building -- Part III. Classification -- Chapter 10. k-Nearest Neighbor Algorithm -- Chapter 11. Decision trees -- Chapter 12. Neural Networks -- Chapter 13. Logistic Regression -- Chapter 14. Naïve Bayes and Bayesian Networks -- Chapter 15. Model Evaluation Techniques -- Chapter 16. Cost-Benefit Analysis Using Data-Driven Costs -- Chapter 17. Cost-Benefit Analysis For Trinary and k-Nary Classification Models -- Chapter 18. Graphical Evaluation of Classification Models -- Part IV. Clustering -- Chapter 19. Hierarchical and k-Means Clustering -- Chapter 20. Kohonen Networks --Chapter 21. Birch Clustering-- Chapter 22. Measuring Cluster Goodness -- Part V. Association Rules -- Chapter 23. Association Rules -- Part VI. Enhancing Model Performance -- Chapter 24. Segmentation Models -- Chapter 25. Ensemble Methods: Bagging and Boosting -- Chapter 26. Model Voting and Propensity Averaging -- Part VII. Further Topics -- Chapter 27. Genetic Algorithms -- Chapter 28. Imputation of Missing Data -- Part VIII. Case Study: Predicting Response to Direct-Mail Marketing --Chapter 29. Case Study, Part 1: Business Understanding, Data Preparation, and Eda -- Chapter 30. Case Study, Part 2: Clustering and Principal Components Analysis -- Chapter 31. Case Study, Part 3: Modeling And Evaluation For Performance And Interpretability -- Chapter 32. Case Study, Part 4: Modeling And Evaluation For High Performance Only.
650
0
a| Data mining.
=| ^A408868
650
0
a| Prediction theory.
=| ^A6714
650
7
a| Manuels.
2| eclas
650
7
a| Fouille de données.
2| eclas
650
7
a| Méthodes statistiques.
2| eclas
650
7
a| Etudes de cas.
2| eclas
650
7
a| Data mining.
2| fast
0| (OCoLC)fst00887946
650
7
a| Prediction theory.
2| fast
0| (OCoLC)fst01075037
700
1
a| Larose, Chantal D.
=| ^A1300384
830
0
a| Wiley series on methods and applications in data mining.
=| ^A1153685
938
a| Brodart
b| BROD
n| 107659824
938
a| Baker and Taylor
b| BTCP
n| BK0013968044
938
a| Coutts Information Services
b| COUT
n| 26441308
938
a| YBP Library Services
b| YANK
n| 11256420
949
a| QA76.9.D343 L3776 2015
h| JOYNER48
o| jssb
i| 30372014721739
994
a| C0
b| ERE
596
a| 1
998
a| 3816144
999
a| QA76.9.D343 L3776 2015
w| LC
c| 1
i| 30372014721739
d| 6/9/2022
e| 6/9/2022
k| CHECKEDOUT
l| JGES
m| JOYNER
n| 5
q| 2
r| M
s| Y
t| JGESBK
u| 8/17/2016
x| BOOK
z| JSTACKS
o| .STAFF. jssb