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Applied data mining for business and industry 2nd ed

Applied data mining for business and industry 2nd ed (2회 대출)

자료유형
단행본
개인저자
Giudici, Paolo. Figini, Silvia.
서명 / 저자사항
Applied data mining for business and industry / Paolo Giudici, Silvia Figini.
판사항
2nd ed.
발행사항
Chichester, U.K. :   Wiley ,   c2009.  
형태사항
viii, 249 p. : ill. ; 24 cm.
ISBN
9780470058862 (cloth) 0470058862 (cloth) 9780470058879 (pbk.) 0470058870 (pbk.)
서지주기
Includes bibliographical references (p. [237]-241) and index.
일반주제명
Data mining. Business -- Data processing. Commercial statistics.
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020 ▼a 0470058862 (cloth)
020 ▼a 9780470058879 (pbk.)
020 ▼a 0470058870 (pbk.)
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100 1 ▼a Giudici, Paolo.
245 1 0 ▼a Applied data mining for business and industry / ▼c Paolo Giudici, Silvia Figini.
250 ▼a 2nd ed.
260 ▼a Chichester, U.K. : ▼b Wiley , ▼c c2009.
300 ▼a viii, 249 p. : ▼b ill. ; ▼c 24 cm.
504 ▼a Includes bibliographical references (p. [237]-241) and index.
650 0 ▼a Data mining.
650 0 ▼a Business ▼x Data processing.
650 0 ▼a Commercial statistics.
700 1 ▼a Figini, Silvia.
945 ▼a KINS

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고6층/ 청구기호 005.74068 G537a2 등록번호 111552778 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차

1 Introduction.

Part I Methodology.

2 Organisation of the data.

2.1 Statistical units and statistical variables.

2.2 Data matrices and their transformations.

2.3 Complex data structures.

2.4 Summary.

3 Summary statistics.

3.1 Univariate exploratory analysis.

3.2 Bivariate exploratory analysis of quantitative data.

3.3 Multivariate exploratory analysis of quantitative data.

3.4 Multivariate exploratory analysis of qualitative data.

3.5 Reduction of dimensionality.

3.6 Further reading.

4 Model specification.

4.1 Measures of distance.

4.2 Cluster analysis.

4.3 Linear regression.

4.4 Logistic regression.

4.5 Tree models.

4.6 Neural networks.

4.7 Nearest-neighbour models.

4.8 Local models.

4.9 Uncertainty measures and inference.

4.10 Non-parametric modelling.

4.11 The normal linear model.

4.12 Generalised linear models.

4.13 Log-linear models.

4.14 Graphical models.

4..15 Survival analysis models.

4.16 Further reading.

5 Model evaluation.

5.1 Criteria based on statistical tests.

5.2 Criteria based on scoring functions.

5.3 Bayesian criteria.

5.4 Computational criteria.

5.5 Criteria based on loss functions.

5.6 Further reading.

Part II Business caste studies.

6 Describing website visitors.

6.1 Objectives of the analysis.

6.2 Description of the data.

6.3 Exploratory analysis.

6.4 Model building.

6.5 Model comparison.

6.6 Summary report.

7 Market basket analysis.

7.1 Objectives of the analysis.

7.2 Description of the data.

7.3 Exploratory data analysis.

7.4 Model building.

7.5 Model comparison.

7.6 Summary report.

8 Describing customer satisfaction.

8.1 Objectives of the analysis.

8.2 Description of the data.

8.3 Exploratory data analysis.

8.4 Model building.

8.5 Summary.

9 Predicting credit risk of small businesses.

9.1 Objectives of the analysis.

9.2 Description of the data.

9.3 Exploratory data analysis.

9.4 Model building.

9.5 Model comparison.

9.6 Summary report.

10 Predicting e-learning student performance.

10.1 Objectives of the analysis.

10.2 Description of the data.

10.3 Exploratory data analysis.

10.4 Model specification.

10.5 Model comparison.

10.6 Summary report.

11 Predicting customer lifetime value.

11.1 Objectives of the analysis.

11.2 Description of the data.

11.3 Exploratory data analysis.

11.4 Model specification.

11.5 Model comparison.

11.6 Summary report.

12 Operational risk management.

12.1 Context and objectives of the analysis.

12.2 Exploratory data analysis.

12.3 Model building.

12.4 Model comparison.

12.5 Summary conclusions.

References.

Index.


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