Applied data mining for business and industry / Paolo Giudici, Silvia Figini.
Giudici, Paolo.
Applied data mining for business and industry / Paolo Giudici, Silvia Figini. - 2nd ed. - Chichester, U.K. : Wiley, c2009. - viii, 249 p. : ill.; 24 cm.
Includes bibliographical references (p. [237]-241) and index.
Introduction -- Part I Methodology -- Organisation of the data -- Statistical units and statistical variables -- Data matrices and their transformations -- Complex data structures -- Summary -- Summary statistics -- Univariate exploratory analysis -- Bivariate exploratory analysis of quantitative data -- Multivariate exploratory analysis of quantitative data -- Multivariate exploratory analysis of qualitative data -- Reduction of dimensionality -- Further reading -- Model specification -- Measures of distance -- Cluster analysis -- Linear regression -- Logistic regression -- Tree models -- Neural networks -- Nearest-neighbour models -- Local models -- Uncertainty measures and inference -- Non-parametric modelling -- The normal linear model -- Generalised linear models -- Log-linear models -- Graphical models -- Survival analysis models -- Further reading -- Model evaluation -- Criteria based on statistical tests -- Criteria based on scoring functions -- Bayesian criteria -- Computational criteria -- Criteria based on loss functions -- Further reading -- Business caste studies -- Describing website visitors -- Objectives of the analysis -- Description of the data -- Exploratory analysis -- Model building -- Model comparison -- Summary report -- Market basket analysis -- Objectives of the analysis -- Description of the data -- Exploratory data analysis -- Model building -- Model comparison -- Summary report -- Describing customer satisfaction -- Objectives of the analysis -- Description of the data -- Exploratory data analysis -- Model building -- Summary -- Predicting credit risk of small businesses -- Objectives of the analysis -- Description of the data -- Exploratory data analysis -- Model building -- Model comparison -- Summary report -- Predicting e-learning student performance -- Objectives of the analysis -- Description of the data -- Exploratory data analysis -- Model specification -- Model comparison -- Summary report -- Predicting customer lifetime value -- Objectives of the analysis -- Description of the data -- Exploratory data analysis -- Model specification -- Model comparison -- Summary report -- Operational risk management -- Context and objectives of the analysis -- Exploratory data analysis -- Model building -- Model comparison -- Summary conclusions -- Rferences -- Index --
Provides an introduction to data mining methods and applications, in a consistent statistical framework. Based upon extensive market research undertaken by the Author the text has been restructured and revised to include 70% new material. Promises to be THE book for professional looking to implement data mining techniques in their business. Includes coverage of classical, multivariate and Bayesian statistical methodology. Uses detailed case studies to illustrate the theory and methodology. Case studies are taken from a range of industries and applications including viticulture, operational risk, genomics and pay-TV services (Sky). Written in an accessible style, aimed at an audience with a basic knowledge of statistics. Discusses the software used in data mining, specifically focusing on SAS, and is supported by a web site featuring data sets, software, and additional material Includes an extensive bibliography and reference section, with suggestions for further readin
9780470058862 0470058862 9780470058879 0470058870
2009008334
Data mining.
Business -- Data processing.
Commercial statistics.
005.74068
Applied data mining for business and industry / Paolo Giudici, Silvia Figini. - 2nd ed. - Chichester, U.K. : Wiley, c2009. - viii, 249 p. : ill.; 24 cm.
Includes bibliographical references (p. [237]-241) and index.
Introduction -- Part I Methodology -- Organisation of the data -- Statistical units and statistical variables -- Data matrices and their transformations -- Complex data structures -- Summary -- Summary statistics -- Univariate exploratory analysis -- Bivariate exploratory analysis of quantitative data -- Multivariate exploratory analysis of quantitative data -- Multivariate exploratory analysis of qualitative data -- Reduction of dimensionality -- Further reading -- Model specification -- Measures of distance -- Cluster analysis -- Linear regression -- Logistic regression -- Tree models -- Neural networks -- Nearest-neighbour models -- Local models -- Uncertainty measures and inference -- Non-parametric modelling -- The normal linear model -- Generalised linear models -- Log-linear models -- Graphical models -- Survival analysis models -- Further reading -- Model evaluation -- Criteria based on statistical tests -- Criteria based on scoring functions -- Bayesian criteria -- Computational criteria -- Criteria based on loss functions -- Further reading -- Business caste studies -- Describing website visitors -- Objectives of the analysis -- Description of the data -- Exploratory analysis -- Model building -- Model comparison -- Summary report -- Market basket analysis -- Objectives of the analysis -- Description of the data -- Exploratory data analysis -- Model building -- Model comparison -- Summary report -- Describing customer satisfaction -- Objectives of the analysis -- Description of the data -- Exploratory data analysis -- Model building -- Summary -- Predicting credit risk of small businesses -- Objectives of the analysis -- Description of the data -- Exploratory data analysis -- Model building -- Model comparison -- Summary report -- Predicting e-learning student performance -- Objectives of the analysis -- Description of the data -- Exploratory data analysis -- Model specification -- Model comparison -- Summary report -- Predicting customer lifetime value -- Objectives of the analysis -- Description of the data -- Exploratory data analysis -- Model specification -- Model comparison -- Summary report -- Operational risk management -- Context and objectives of the analysis -- Exploratory data analysis -- Model building -- Model comparison -- Summary conclusions -- Rferences -- Index --
Provides an introduction to data mining methods and applications, in a consistent statistical framework. Based upon extensive market research undertaken by the Author the text has been restructured and revised to include 70% new material. Promises to be THE book for professional looking to implement data mining techniques in their business. Includes coverage of classical, multivariate and Bayesian statistical methodology. Uses detailed case studies to illustrate the theory and methodology. Case studies are taken from a range of industries and applications including viticulture, operational risk, genomics and pay-TV services (Sky). Written in an accessible style, aimed at an audience with a basic knowledge of statistics. Discusses the software used in data mining, specifically focusing on SAS, and is supported by a web site featuring data sets, software, and additional material Includes an extensive bibliography and reference section, with suggestions for further readin
9780470058862 0470058862 9780470058879 0470058870
2009008334
Data mining.
Business -- Data processing.
Commercial statistics.
005.74068