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Guide to intelligent data analysis : how to intelligently make sense of real data / Michael R. Berthold ... [et al.]

Colaborador(es): Tipo de material: TextoTextoSeries Texts in computer scienceDetalles de publicación: New York : Springer, ©2010Descripción: xiii, 394 páginas. : ilustraciones, graficas. ; 24 x 17 centímetrosTipo de contenido:
  • texto
Tipo de medio:
  • sin medio
Tipo de soporte:
  • volumen
ISBN:
  • 1848822596
  • 9781848822597
Tema(s): Clasificación LoC:
  • QA 276 G83
Contenidos:
Introduction -- Motivation -- The analysis process -- Methods, tasks, and tools -- How to read this book -- Practical data analysis : an example -- The setup -- Data understanding and pattern finding -- Explanation finding -- Predicting the future -- Concluding remarks -- Project understanding -- Determine the project objective -- Assess the situation -- Determine analysis goals -- Further reading -- Data understanding -- Attribute understanding -- Data quality -- Data visualization -- Correlation analysis -- Outlier detection -- Missing values -- A checklist for data understanding -- Data understanding in practice -- Principles of modeling -- Model classes -- Fitting criteria and score functions -- Algorithms for model fitting -- Types of errors -- Model validation -- Model errors and validation in practice -- Further reading -- Data preparation -- Select data -- Clean data -- Construct data -- Complex data types -- Data integration -- Data preparation in practice -- Finding patterns -- Hierarchical clustering -- Notion of (Dis-) Similarity -- Prototype- and model-based clustering -- Density-based clustering -- Self-organizing maps -- Frequent pattern mining and association rules -- Deviation analysis -- Finding patterns in practice -- Further reading -- Finding explanations -- Decision trees -- Bayes classifiers -- Regression -- Rule learning -- Finding explanations in practice -- Further reading -- Finding predictors -- Nearest-neighbor predictors -- Artifical neural networks -- Support vector machines -- Ensemble methods -- Finding predictors in practice -- Evaluation and deployment -- Evaluation -- Deployment and monitoring -- Statistics -- Terms and notation -- Descriptive statistics -- Probability theory -- Inferential statistics -- The R project -- KNIME
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Tipo de ítem Biblioteca actual Biblioteca de origen Colección Signatura topográfica Copia número Estado Notas Fecha de vencimiento Código de barras Reserva de ítems
Libros para consulta en sala Libros para consulta en sala Biblioteca Antonio Enriquez Savignac Biblioteca Antonio Enriquez Savignac COLECCIÓN RESERVA QA 276 G83 (Navegar estantería(Abre debajo)) 1 No para préstamo Ingeniería Telemática 029122
Libros Libros Biblioteca Antonio Enriquez Savignac Biblioteca Antonio Enriquez Savignac Colección General QA 276 G83 (Navegar estantería(Abre debajo)) 2 Disponible Ingeniería Telemática 038233
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Incluye referencias bibliográficas e índice

Introduction -- Motivation -- The analysis process -- Methods, tasks, and tools -- How to read this book -- Practical data analysis : an example -- The setup -- Data understanding and pattern finding -- Explanation finding -- Predicting the future -- Concluding remarks -- Project understanding -- Determine the project objective -- Assess the situation -- Determine analysis goals -- Further reading -- Data understanding -- Attribute understanding -- Data quality -- Data visualization -- Correlation analysis -- Outlier detection -- Missing values -- A checklist for data understanding -- Data understanding in practice -- Principles of modeling -- Model classes -- Fitting criteria and score functions -- Algorithms for model fitting -- Types of errors -- Model validation -- Model errors and validation in practice -- Further reading -- Data preparation -- Select data -- Clean data -- Construct data -- Complex data types -- Data integration -- Data preparation in practice -- Finding patterns -- Hierarchical clustering -- Notion of (Dis-) Similarity -- Prototype- and model-based clustering -- Density-based clustering -- Self-organizing maps -- Frequent pattern mining and association rules -- Deviation analysis -- Finding patterns in practice -- Further reading -- Finding explanations -- Decision trees -- Bayes classifiers -- Regression -- Rule learning -- Finding explanations in practice -- Further reading -- Finding predictors -- Nearest-neighbor predictors -- Artifical neural networks -- Support vector machines -- Ensemble methods -- Finding predictors in practice -- Evaluation and deployment -- Evaluation -- Deployment and monitoring -- Statistics -- Terms and notation -- Descriptive statistics -- Probability theory -- Inferential statistics -- The R project -- KNIME

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