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Link de la búsquedaAdvances in Research Methods for Information Systems Research / SpringerLink (Online service) ; Kweku-Muata Osei-Bryson ; Ojelanki Ngwenyama (2014)
Título : Advances in Research Methods for Information Systems Research : Data Mining, Data Envelopment Analysis, Value Focused Thinking Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Kweku-Muata Osei-Bryson ; Ojelanki Ngwenyama Editorial: Boston, MA : Springer US Fecha de publicación: 2014 Otro editor: Imprint: Springer Colección: Integrated Series in Information Systems, ISSN 1571-0270 num. 34 Número de páginas: VII, 231 p. 52 illus., 30 illus. in color Il.: online resource ISBN/ISSN/DL: 978-1-4614-9463-8 Idioma : Inglés (eng) Palabras clave: Business Information technology Data processing Computers mining and Management IT in Mining Knowledge Discovery Systems Communication Service Clasificación: 658 Empresas. Organización de empresas Resumen: Advances in social science research methodologies and data analytic methods are changing the way research in information systems is conducted. New developments in statistical software technologies for data mining (DM) such as regression splines or decision tree induction can be used to assist researchers in systematic post-positivist theory testing and development. Established management science techniques like data envelopment analysis (DEA), and value focused thinking (VFT) can be used in combination with traditional statistical analysis and data mining techniques to more effectively explore behavioral questions in information systems research. As adoption and use of these research methods expand, there is growing need for a resource book to assist doctoral students and advanced researchers in understanding their potential to contribute to a broad range of research problems. Advances in Research Methods for Information Systems Research: Data Mining, Data Envelopment Analysis, Value Focused Thinking focuses on bridging and unifying these three different methodologies in order to bring them together in a unified volume for the information systems community. This book serves as a resource that provides overviews on each method, as well as applications on how they can be employed to address IS research problems. Its goal is to help researchers in their continuous efforts to set the pace for having an appropriate interplay between behavioral research and design science Nota de contenido: Introduction -- Logical Foundations of Social Science Research -- Overview on Decision Tree Induction -- Using Decision Tree Induction for Theory Development -- A Hybrid Decision Tree-based Method for Exploring Cumulative Abnormal Returns -- An Ethnographic Decision Tree Modeling: An Exploration of Telecentre Usage in the Human Development Context -- Using Association Rules Mining to Facilitate Qualitative Data Analysis in Theory Building -- Overview on Multivariate Adaptive Regression Splines -- Reexamining the Impact of Information Technology Investment on Productivity Using Regression Tree and MARS -- Overview on Cluster Analysis -- Overview on Data Envelopment Analysis -- Exploring the ICT Utilization using Data Envelopment Analysis -- A DEA-centric Decision Support System for Monitoring Efficiency-Based Performance -- Overview on the Value Focused Thinking Methodology -- Using Value Focused Thinking to Develop Performance Criteria & Measures for Information Systems Projects En línea: http://dx.doi.org/10.1007/978-1-4614-9463-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35928 Advances in Research Methods for Information Systems Research : Data Mining, Data Envelopment Analysis, Value Focused Thinking [documento electrónico] / SpringerLink (Online service) ; Kweku-Muata Osei-Bryson ; Ojelanki Ngwenyama . - Boston, MA : Springer US : Imprint: Springer, 2014 . - VII, 231 p. 52 illus., 30 illus. in color : online resource. - (Integrated Series in Information Systems, ISSN 1571-0270; 34) .
ISBN : 978-1-4614-9463-8
Idioma : Inglés (eng)
Palabras clave: Business Information technology Data processing Computers mining and Management IT in Mining Knowledge Discovery Systems Communication Service Clasificación: 658 Empresas. Organización de empresas Resumen: Advances in social science research methodologies and data analytic methods are changing the way research in information systems is conducted. New developments in statistical software technologies for data mining (DM) such as regression splines or decision tree induction can be used to assist researchers in systematic post-positivist theory testing and development. Established management science techniques like data envelopment analysis (DEA), and value focused thinking (VFT) can be used in combination with traditional statistical analysis and data mining techniques to more effectively explore behavioral questions in information systems research. As adoption and use of these research methods expand, there is growing need for a resource book to assist doctoral students and advanced researchers in understanding their potential to contribute to a broad range of research problems. Advances in Research Methods for Information Systems Research: Data Mining, Data Envelopment Analysis, Value Focused Thinking focuses on bridging and unifying these three different methodologies in order to bring them together in a unified volume for the information systems community. This book serves as a resource that provides overviews on each method, as well as applications on how they can be employed to address IS research problems. Its goal is to help researchers in their continuous efforts to set the pace for having an appropriate interplay between behavioral research and design science Nota de contenido: Introduction -- Logical Foundations of Social Science Research -- Overview on Decision Tree Induction -- Using Decision Tree Induction for Theory Development -- A Hybrid Decision Tree-based Method for Exploring Cumulative Abnormal Returns -- An Ethnographic Decision Tree Modeling: An Exploration of Telecentre Usage in the Human Development Context -- Using Association Rules Mining to Facilitate Qualitative Data Analysis in Theory Building -- Overview on Multivariate Adaptive Regression Splines -- Reexamining the Impact of Information Technology Investment on Productivity Using Regression Tree and MARS -- Overview on Cluster Analysis -- Overview on Data Envelopment Analysis -- Exploring the ICT Utilization using Data Envelopment Analysis -- A DEA-centric Decision Support System for Monitoring Efficiency-Based Performance -- Overview on the Value Focused Thinking Methodology -- Using Value Focused Thinking to Develop Performance Criteria & Measures for Information Systems Projects En línea: http://dx.doi.org/10.1007/978-1-4614-9463-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35928 Ejemplares
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Título : Classification and Data Mining Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Antonio Giusti ; Gunter Ritter ; Maurizio Vichi Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2013 Otro editor: Imprint: Springer Colección: Studies in Classification, Data Analysis, and Knowledge Organization, ISSN 1431-8814 Número de páginas: XIV, 286 p. 85 illus., 49 illus. in color Il.: online resource ISBN/ISSN/DL: 978-3-642-28894-4 Idioma : Inglés (eng) Palabras clave: Statistics Database management Data mining Statistical Theory and Methods Management Mining Knowledge Discovery Clasificación: 51 Matemáticas Resumen: This volume contains both methodological papers showing new original methods, and papers on applications illustrating how new domain-specific knowledge can be made available from data by clever use of data analysis methods. The volume is subdivided in three parts: Classification and Data Analysis; Data Mining; and Applications. The selection of peer reviewed papers had been presented at a meeting of classification societies held in Florence, Italy, in the area of "Classification and Data Mining" Nota de contenido: Classification and Data Analysis -- Data Mining -- Applications En línea: http://dx.doi.org/10.1007/978-3-642-28894-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32498 Classification and Data Mining [documento electrónico] / SpringerLink (Online service) ; Antonio Giusti ; Gunter Ritter ; Maurizio Vichi . - Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013 . - XIV, 286 p. 85 illus., 49 illus. in color : online resource. - (Studies in Classification, Data Analysis, and Knowledge Organization, ISSN 1431-8814) .
ISBN : 978-3-642-28894-4
Idioma : Inglés (eng)
Palabras clave: Statistics Database management Data mining Statistical Theory and Methods Management Mining Knowledge Discovery Clasificación: 51 Matemáticas Resumen: This volume contains both methodological papers showing new original methods, and papers on applications illustrating how new domain-specific knowledge can be made available from data by clever use of data analysis methods. The volume is subdivided in three parts: Classification and Data Analysis; Data Mining; and Applications. The selection of peer reviewed papers had been presented at a meeting of classification societies held in Florence, Italy, in the area of "Classification and Data Mining" Nota de contenido: Classification and Data Analysis -- Data Mining -- Applications En línea: http://dx.doi.org/10.1007/978-3-642-28894-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32498 Ejemplares
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Título : Data Mining and Knowledge Discovery via Logic-Based Methods : Theory, Algorithms, and Applications Tipo de documento: documento electrónico Autores: Evangelos Triantaphyllou ; SpringerLink (Online service) Editorial: Boston, MA : Springer US Fecha de publicación: 2010 Colección: Springer Optimization and Its Applications, ISSN 1931-6828 num. 43 Número de páginas: XXXIV, 350 p. 91 illus., 9 illus. in color Il.: online resource ISBN/ISSN/DL: 978-1-4419-1630-3 Idioma : Inglés (eng) Palabras clave: Computer science Operations research Decision making logic Mathematical Data mining Management Science Mining and Knowledge Discovery Logics Meanings of Programs Logic Foundations Research, Formal Languages Operation Research/Decision Theory Clasificación: 51 Matemáticas Resumen: The importance of having efficient and effective methods for data mining and knowledge discovery (DM) is rapidly growing. This is due to the wide use of fast and affordable computing power and data storage media and also the gathering of huge amounts of data in almost all aspects of human activity and interest. While numerous methods have been developed, the focus of this book presents algorithms and applications using one popular method that has been formulated in terms of binary attributes, i.e., by Boolean functions defined on several attributes that are easily transformed into rules that can express new knowledge. This book presents methods that deal with key data mining and knowledge discovery issues in an intuitive manner, in a natural sequence, and in a way that can be easily understood and interpreted by a wide array of experts and end users. The presentation provides a unique perspective into the essence of some fundamental DM issues, many of which come from important real life applications such as breast cancer diagnosis. Applications and algorithms are accompanied by extensive experimental results and are presented in a way such that anyone with a minimum background in mathematics and computer science can benefit from the exposition. Rigor in mathematics and algorithmic development is not compromised and each chapter systematically offers some possible extensions for future research Nota de contenido: Algorithmic Issues -- Inferring a Boolean Function from Positive and Negative Examples -- A Revised Branch-and-Bound Approach for Inferring a Boolean Function from Examples -- Some Fast Heuristics for Inferring a Boolean Function from Examples -- An Approach to Guided Learning of Boolean Functions -- An Incremental Learning Algorithm for Inferring Boolean Functions -- A Duality Relationship Between Boolean Functions in CNF and DNF Derivable from the Same Training Examples -- The Rejectability Graph of Two Sets of Examples -- Application Issues -- The Reliability Issue in Data Mining: The Case of Computer-Aided Breast Cancer Diagnosis -- Data Mining and Knowledge Discovery by Means of Monotone Boolean Functions -- Some Application Issues of Monotone Boolean Functions -- Mining of Association Rules -- Data Mining of Text Documents -- First Case Study: Predicting Muscle Fatigue from EMG Signals -- Second Case Study: Inference of Diagnostic Rules for Breast Cancer -- A Fuzzy Logic Approach to Attribute Formalization: Analysis of Lobulation for Breast Cancer Diagnosis -- Conclusions En línea: http://dx.doi.org/10.1007/978-1-4419-1630-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33602 Data Mining and Knowledge Discovery via Logic-Based Methods : Theory, Algorithms, and Applications [documento electrónico] / Evangelos Triantaphyllou ; SpringerLink (Online service) . - Boston, MA : Springer US, 2010 . - XXXIV, 350 p. 91 illus., 9 illus. in color : online resource. - (Springer Optimization and Its Applications, ISSN 1931-6828; 43) .
ISBN : 978-1-4419-1630-3
Idioma : Inglés (eng)
Palabras clave: Computer science Operations research Decision making logic Mathematical Data mining Management Science Mining and Knowledge Discovery Logics Meanings of Programs Logic Foundations Research, Formal Languages Operation Research/Decision Theory Clasificación: 51 Matemáticas Resumen: The importance of having efficient and effective methods for data mining and knowledge discovery (DM) is rapidly growing. This is due to the wide use of fast and affordable computing power and data storage media and also the gathering of huge amounts of data in almost all aspects of human activity and interest. While numerous methods have been developed, the focus of this book presents algorithms and applications using one popular method that has been formulated in terms of binary attributes, i.e., by Boolean functions defined on several attributes that are easily transformed into rules that can express new knowledge. This book presents methods that deal with key data mining and knowledge discovery issues in an intuitive manner, in a natural sequence, and in a way that can be easily understood and interpreted by a wide array of experts and end users. The presentation provides a unique perspective into the essence of some fundamental DM issues, many of which come from important real life applications such as breast cancer diagnosis. Applications and algorithms are accompanied by extensive experimental results and are presented in a way such that anyone with a minimum background in mathematics and computer science can benefit from the exposition. Rigor in mathematics and algorithmic development is not compromised and each chapter systematically offers some possible extensions for future research Nota de contenido: Algorithmic Issues -- Inferring a Boolean Function from Positive and Negative Examples -- A Revised Branch-and-Bound Approach for Inferring a Boolean Function from Examples -- Some Fast Heuristics for Inferring a Boolean Function from Examples -- An Approach to Guided Learning of Boolean Functions -- An Incremental Learning Algorithm for Inferring Boolean Functions -- A Duality Relationship Between Boolean Functions in CNF and DNF Derivable from the Same Training Examples -- The Rejectability Graph of Two Sets of Examples -- Application Issues -- The Reliability Issue in Data Mining: The Case of Computer-Aided Breast Cancer Diagnosis -- Data Mining and Knowledge Discovery by Means of Monotone Boolean Functions -- Some Application Issues of Monotone Boolean Functions -- Mining of Association Rules -- Data Mining of Text Documents -- First Case Study: Predicting Muscle Fatigue from EMG Signals -- Second Case Study: Inference of Diagnostic Rules for Breast Cancer -- A Fuzzy Logic Approach to Attribute Formalization: Analysis of Lobulation for Breast Cancer Diagnosis -- Conclusions En línea: http://dx.doi.org/10.1007/978-1-4419-1630-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33602 Ejemplares
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Título : Data Mining for Biomarker Discovery Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Panos M. Pardalos ; Petros Xanthopoulos ; Michalis Zervakis Editorial: Boston, MA : Springer US Fecha de publicación: 2012 Colección: Springer Optimization and Its Applications, ISSN 1931-6828 num. 65 Número de páginas: XIV, 246 p Il.: online resource ISBN/ISSN/DL: 978-1-4614-2107-8 Idioma : Inglés (eng) Palabras clave: Mathematics Biochemical engineering Health informatics Data mining Operations research Management science Research, Science Mining and Knowledge Discovery Informatics Engineering Clasificación: 51 Matemáticas Resumen: Data Mining for Biomarker Discovery is designed to motivate collaboration and discussion among various disciplines and will be of interest to students and researchers in engineering, computer science, applied mathematics, medicine, and anyone interested in the interdisciplinary application of data mining techniques. Biomarker discovery is an important area of biomedical research that can lead to significant breakthroughs in disease analysis and targeted therapy. Moreover, the discovery and management of new biomarkers is a challenging and attractive problem in the emerging field of biomedical informatics. This volume is a collection of state-of-the-art research from select participants of the “International Conference on Biomedical Data and Knowledge Mining: Towards Biomarker Discovery,” held July 7-9, 2010 in Chania, Greece. Contributions focus on biomarker data integration, information retrieval methods, and statistical machine learning techniques, all presented with new results, models, and algorithms Nota de contenido: Preface -- 1. Data Mining Strategies Applied in Brain Injury Models (S. Mondello, F. Kobeissy, I. Fingers, Z. Zhang, R.L. Hayes, K.K.W. Wang) -- Application of Decomposition Methods in the Filtering of Event Related Potentials (K. Michalopoulos, V. Iordanidou, M. Zervakis) -- 3. EEG Features as Biomarkers for Discrimination of Pre-ictal states (A. Tsimpiris, D. Kugiumtzis) -- 4. Using Relative Power Asymmetry as a Biomarker for Classifying Psychogenic Non-epileptic Seizure and Complex Partial Seizure Patients (J.H. Chien, D.-S. Shiau, J.C. Sackellares, J.J. Halford, K.M. Kelly, P.M. Pardalos) -- 5. Classification of Tree and Network Topology Structures in Medical Images (A. Skoura, V. Megalooikonomou, A. Diamantopolous, G.C. Kagadis, D. Karnabatidis) -- 6. A Framework for Multi-Modal Imagin Biomarker Extraction with Application to Brain MRI (K. Maria, V. Sakkalis, N. Graf) -- 7. A Statistical Diagnostic Decision Support Tool Using Magnetic Resonance Spectroscopy Data (E. Tsolaki, E. Kousi, E. Kapsalaki, I. Dimou, K. Theodorou, G. C. Manikis, C. Kappas, I. Tsougos) -- 8. Data Mining for Cancer Biomarkers with Raman Spectroscopy (M.B.Fenn, V. Pappu) -- 9. Nonlinear Recognition Methods for Oncological Pathologies (G. Patrizi, V. Pietropaolo, A. Carbone, R. De Leone, L. Di Giacomo, V. Losaco, G. Patrizi) -- 10. Studying Connectivity Properties in Human Protein Interation Network in Cancer Pathway (V. Tomaino, A. Arulselvan, P. Veltri, P.M. Pardalos) -- 11. Modeling of Oral Cancer Progression Using Dynamic Bayesian Networks (K.P. Exarchos, G. Rigas, Y. Golestsis, D.I. Fotiadis) -- 12. Neuromuscular Alterations of Upper Airway Muscles in Patients with OSAS Radiological and Histopathological Findings (P. Drakatos, D. Lykouras, F. Sampsonas, K. Karkoulias, K. Spiropoulos) -- 13. Data Mining System Applied to Population Databases for Studies on Lung Cancer (J. Pérez, F. Henriques, R. Santaolaya, O. Fragoso, A. Mexicano) En línea: http://dx.doi.org/10.1007/978-1-4614-2107-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32782 Data Mining for Biomarker Discovery [documento electrónico] / SpringerLink (Online service) ; Panos M. Pardalos ; Petros Xanthopoulos ; Michalis Zervakis . - Boston, MA : Springer US, 2012 . - XIV, 246 p : online resource. - (Springer Optimization and Its Applications, ISSN 1931-6828; 65) .
ISBN : 978-1-4614-2107-8
Idioma : Inglés (eng)
Palabras clave: Mathematics Biochemical engineering Health informatics Data mining Operations research Management science Research, Science Mining and Knowledge Discovery Informatics Engineering Clasificación: 51 Matemáticas Resumen: Data Mining for Biomarker Discovery is designed to motivate collaboration and discussion among various disciplines and will be of interest to students and researchers in engineering, computer science, applied mathematics, medicine, and anyone interested in the interdisciplinary application of data mining techniques. Biomarker discovery is an important area of biomedical research that can lead to significant breakthroughs in disease analysis and targeted therapy. Moreover, the discovery and management of new biomarkers is a challenging and attractive problem in the emerging field of biomedical informatics. This volume is a collection of state-of-the-art research from select participants of the “International Conference on Biomedical Data and Knowledge Mining: Towards Biomarker Discovery,” held July 7-9, 2010 in Chania, Greece. Contributions focus on biomarker data integration, information retrieval methods, and statistical machine learning techniques, all presented with new results, models, and algorithms Nota de contenido: Preface -- 1. Data Mining Strategies Applied in Brain Injury Models (S. Mondello, F. Kobeissy, I. Fingers, Z. Zhang, R.L. Hayes, K.K.W. Wang) -- Application of Decomposition Methods in the Filtering of Event Related Potentials (K. Michalopoulos, V. Iordanidou, M. Zervakis) -- 3. EEG Features as Biomarkers for Discrimination of Pre-ictal states (A. Tsimpiris, D. Kugiumtzis) -- 4. Using Relative Power Asymmetry as a Biomarker for Classifying Psychogenic Non-epileptic Seizure and Complex Partial Seizure Patients (J.H. Chien, D.-S. Shiau, J.C. Sackellares, J.J. Halford, K.M. Kelly, P.M. Pardalos) -- 5. Classification of Tree and Network Topology Structures in Medical Images (A. Skoura, V. Megalooikonomou, A. Diamantopolous, G.C. Kagadis, D. Karnabatidis) -- 6. A Framework for Multi-Modal Imagin Biomarker Extraction with Application to Brain MRI (K. Maria, V. Sakkalis, N. Graf) -- 7. A Statistical Diagnostic Decision Support Tool Using Magnetic Resonance Spectroscopy Data (E. Tsolaki, E. Kousi, E. Kapsalaki, I. Dimou, K. Theodorou, G. C. Manikis, C. Kappas, I. Tsougos) -- 8. Data Mining for Cancer Biomarkers with Raman Spectroscopy (M.B.Fenn, V. Pappu) -- 9. Nonlinear Recognition Methods for Oncological Pathologies (G. Patrizi, V. Pietropaolo, A. Carbone, R. De Leone, L. Di Giacomo, V. Losaco, G. Patrizi) -- 10. Studying Connectivity Properties in Human Protein Interation Network in Cancer Pathway (V. Tomaino, A. Arulselvan, P. Veltri, P.M. Pardalos) -- 11. Modeling of Oral Cancer Progression Using Dynamic Bayesian Networks (K.P. Exarchos, G. Rigas, Y. Golestsis, D.I. Fotiadis) -- 12. Neuromuscular Alterations of Upper Airway Muscles in Patients with OSAS Radiological and Histopathological Findings (P. Drakatos, D. Lykouras, F. Sampsonas, K. Karkoulias, K. Spiropoulos) -- 13. Data Mining System Applied to Population Databases for Studies on Lung Cancer (J. Pérez, F. Henriques, R. Santaolaya, O. Fragoso, A. Mexicano) En línea: http://dx.doi.org/10.1007/978-1-4614-2107-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32782 Ejemplares
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Título : Data Mining in Agriculture Tipo de documento: documento electrónico Autores: Antonio Mucherino ; SpringerLink (Online service) ; Petraq J. Papajorgji ; Panos M. Pardalos Editorial: New York, NY : Springer New York Fecha de publicación: 2009 Colección: Springer Optimization and Its Applications, ISSN 1931-6828 num. 34 Número de páginas: XVIII, 274 p. 92 illus Il.: online resource ISBN/ISSN/DL: 978-0-387-88615-2 Idioma : Inglés (eng) Palabras clave: Mathematics Data mining Agriculture Mathematical models Operations research Management science Environmental sciences Modeling and Industrial Mining Knowledge Discovery Research, Science Math. Appl. in Clasificación: 51 Matemáticas Resumen: Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®. Examples and exercises with solutions are provided at the end of each chapter to facilitate the comprehension of the material. For each data mining technique described in the book variants and improvements of the basic algorithm are also given. Also by P.J. Papajorgji and P.M. Pardalos: Advances in Modeling Agricultural Systems, 'Springer Optimization and its Applications' vol. 25, ©2009 Nota de contenido: to Data Mining -- Statistical Based Approaches -- Clustering by -means -- -Nearest Neighbor Classification -- Artificial Neural Networks -- Support Vector Machines -- Biclustering -- Validation -- Data Mining in a Parallel Environment -- Solutions to Exercises En línea: http://dx.doi.org/10.1007/978-0-387-88615-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33912 Data Mining in Agriculture [documento electrónico] / Antonio Mucherino ; SpringerLink (Online service) ; Petraq J. Papajorgji ; Panos M. Pardalos . - New York, NY : Springer New York, 2009 . - XVIII, 274 p. 92 illus : online resource. - (Springer Optimization and Its Applications, ISSN 1931-6828; 34) .
ISBN : 978-0-387-88615-2
Idioma : Inglés (eng)
Palabras clave: Mathematics Data mining Agriculture Mathematical models Operations research Management science Environmental sciences Modeling and Industrial Mining Knowledge Discovery Research, Science Math. Appl. in Clasificación: 51 Matemáticas Resumen: Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®. Examples and exercises with solutions are provided at the end of each chapter to facilitate the comprehension of the material. For each data mining technique described in the book variants and improvements of the basic algorithm are also given. Also by P.J. Papajorgji and P.M. Pardalos: Advances in Modeling Agricultural Systems, 'Springer Optimization and its Applications' vol. 25, ©2009 Nota de contenido: to Data Mining -- Statistical Based Approaches -- Clustering by -means -- -Nearest Neighbor Classification -- Artificial Neural Networks -- Support Vector Machines -- Biclustering -- Validation -- Data Mining in a Parallel Environment -- Solutions to Exercises En línea: http://dx.doi.org/10.1007/978-0-387-88615-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33912 Ejemplares
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