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Título : Corporate Knowledge Discovery and Organizational Learning : The Role, Importance, and Application of Semantic Business Process Management Tipo de documento: documento electrónico Autores: András Gábor ; SpringerLink (Online service) ; Andrea Ko Editorial: Cham : Springer International Publishing Fecha de publicación: 2016 Otro editor: Imprint: Springer Colección: Knowledge Management and Organizational Learning, ISSN 2199-8663 num. 2 Número de páginas: XI, 173 p. 71 illus., 62 illus. in color Il.: online resource ISBN/ISSN/DL: 978-3-319-28917-5 Idioma : Inglés (eng) Palabras clave: Business Knowledge management Management information systems Data mining and Mining Discovery Information Systems Clasificación: 658.011.8 Gestión del conocimiento. Innovación y mejoras en las organizaciones Resumen: This book investigates organizational learning from a variety of information processing perspectives. Continuous change and complexity in regulatory, social and economic environments are increasingly forcing organizations and their employees to acquire the necessary job-specific knowledge at the right time and in the right format. Though many regulatory documents are now available in digital form, their complexity and diversity make identifying the relevant elements for a particular context a challenging task. In such scenarios, business processes tend to be important sources of knowledge, containing rich but in many cases embedded, hidden knowledge. This book discusses the possible connection between business process models and corporate knowledge assets; knowledge extraction approaches based on organizational processes; developing and maintaining corporate knowledge bases; and semantic business process management and its relation to organizational learning approaches. The individual chapters reveal the different elements of a knowledge management solution designed to extract, organize and preserve the knowledge embedded in business processes so as to: enrich organizational knowledge bases in a systematic and controlled way, support employees in acquiring job role-specific knowledge, promote organizational learning, and steer human capital investment. All of these topics are analyzed on the basis of real-world cases from the domains of insurance, food safety, innovation, and funding Nota de contenido: Corporate Knowledge Discovery and Organizational Learning - The Role, Importance, and Application of Semantic Business Process Management - The ProKEX Case -- Corporate Semantic Business Process Management -- ProMine: A Text Mining Solution for Concept Extraction and Filtering -- STUDIO - Ontology-Centric Knowledge-Based System -- Ontology Tailoring for Job Role Knowledge -- STUDIO: A Solution on Adaptive Testing -- Future Development: Towards Semantic Compliance Checking En línea: http://dx.doi.org/10.1007/978-3-319-28917-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=41590 Corporate Knowledge Discovery and Organizational Learning : The Role, Importance, and Application of Semantic Business Process Management [documento electrónico] / András Gábor ; SpringerLink (Online service) ; Andrea Ko . - Cham : Springer International Publishing : Imprint: Springer, 2016 . - XI, 173 p. 71 illus., 62 illus. in color : online resource. - (Knowledge Management and Organizational Learning, ISSN 2199-8663; 2) .
ISBN : 978-3-319-28917-5
Idioma : Inglés (eng)
Palabras clave: Business Knowledge management Management information systems Data mining and Mining Discovery Information Systems Clasificación: 658.011.8 Gestión del conocimiento. Innovación y mejoras en las organizaciones Resumen: This book investigates organizational learning from a variety of information processing perspectives. Continuous change and complexity in regulatory, social and economic environments are increasingly forcing organizations and their employees to acquire the necessary job-specific knowledge at the right time and in the right format. Though many regulatory documents are now available in digital form, their complexity and diversity make identifying the relevant elements for a particular context a challenging task. In such scenarios, business processes tend to be important sources of knowledge, containing rich but in many cases embedded, hidden knowledge. This book discusses the possible connection between business process models and corporate knowledge assets; knowledge extraction approaches based on organizational processes; developing and maintaining corporate knowledge bases; and semantic business process management and its relation to organizational learning approaches. The individual chapters reveal the different elements of a knowledge management solution designed to extract, organize and preserve the knowledge embedded in business processes so as to: enrich organizational knowledge bases in a systematic and controlled way, support employees in acquiring job role-specific knowledge, promote organizational learning, and steer human capital investment. All of these topics are analyzed on the basis of real-world cases from the domains of insurance, food safety, innovation, and funding Nota de contenido: Corporate Knowledge Discovery and Organizational Learning - The Role, Importance, and Application of Semantic Business Process Management - The ProKEX Case -- Corporate Semantic Business Process Management -- ProMine: A Text Mining Solution for Concept Extraction and Filtering -- STUDIO - Ontology-Centric Knowledge-Based System -- Ontology Tailoring for Job Role Knowledge -- STUDIO: A Solution on Adaptive Testing -- Future Development: Towards Semantic Compliance Checking En línea: http://dx.doi.org/10.1007/978-3-319-28917-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=41590 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 : Real World Data Mining Applications Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Mahmoud Abou-Nasr ; Stefan Lessmann ; Stahlbock, Robert ; Gary M. Weiss Editorial: Cham : Springer International Publishing Fecha de publicación: 2015 Otro editor: Imprint: Springer Colección: Annals of Information Systems, ISSN 1934-3221 num. 17 Número de páginas: XVI, 418 p. 144 illus., 96 illus. in color Il.: online resource ISBN/ISSN/DL: 978-3-319-07812-0 Idioma : Inglés (eng) Palabras clave: Business Operations research Decision making Information technology Data processing mining and Management IT in Operation Research/Decision Theory Mining Knowledge Discovery Clasificación: 658 Empresas. Organización de empresas Resumen: Introduction Mahmoud Abou-Nasr, Stefan Lessmann. Robert Stahlbock, Gary M. Weiss What Data Scientists can Learn from History Aaron Lai On Line Mining of Cyclic Association Rules From Parallel Dimension Hierarchies Eya Ben Ahmed, Ahlem Nabli, Faiez Gargouri PROFIT: A Projected Clustering Technique Dharmveer Singh Rajput, Pramod Kumar Singh, Mahua Bhattacharya Multi-Label Classification with a Constrained Minimum Cut Model Guangzhi Qu, Ishwar Sethi, Craig Hartrick, Hui Zhang On the Selection of Dimension Reduction Techniques for Scientific Applications Ya Ju Fan, Chandrika Kamath Relearning Process for SPRT in Structural Change Detection of Time-Series Data Ryosuke Saga, Naoki Kaisaku, Hiroshi Tsuji K-means clustering on a classifier-induced representation space: application to customer contact personalization Vincent Lemaire, Fabrice Clerot, Nicolas Creff Dimensionality Reduction using Graph Weighted Subspace Learning for Bankruptcy Prediction Bernardete Ribeiro, Ning Chen Click Fraud Detection: Adversarial Pattern Recognition over 5 Years at Microsoft Brendan Kitts, Jing Ying Zhang, Gang Wu, Wesley Brandi, Julien Beasley, Kieran Morrill, John Ettedgui, Sid Siddhartha, Hong Yuan, Feng Gao, Peter Azo, Raj Mahato A Novel Approach for Analysis of ’Real World’ Data: A Data Mining Engine for Identification of Multi-author Student Document Submission Kathryn Burn-Thornton, Tim Burman Data Mining Based Tax Audit Selection: A Case Study of a Pilot Project at the Minnesota Department of Revenue Kuo-Wei Hsu, Nishith Pathak, Jaideep Srivastava, Greg Tschida, Eric Bjorklund A nearest neighbor approach to build a readable risk score for breast cancer Emilien Gauthier, Laurent Brisson, Philippe Lenca, Stephane Ragusa Machine Learning for Medical Examination Report Processing Yinghao Huang, Yi Lu Murphey, Naeem Seliya, Roy B. Friedenthal Data Mining Vortex Cores Concurrent with Computational Fluid Dynamics Simulations Clifton Mortensen, Steve Gorrell, Robert Woodley, Michael Gosnell A Data Mining Based Method for Discovery of Web Services and their Compositions Richi Nayak, Aishwarya Bose Exploiting Terrain Information for Enhancing Fuel Economy of Cruising Vehicles by Supervised Training of Recurrent Neural Optimizers Mahmoud Abou-Nasr, John Michelini, Dimitar Filev Exploration of Flight State and Control System Parameters for Prediction of Helicopter Loads via Gamma Test and Machine Learning Techniques Catherine Cheung, Julio J. Valdes, Matthew Li Multilayer Semantic Analysis In Image Databases Ismail El Sayad, Jean Martinet, Zhongfei (Mark) Zhang, Peter Eisert Nota de contenido: Introduction -- What Data Scientists can Learn from History -- On Line Mining of Cyclic Association Rules From Parallel Dimension Hierarchies -- PROFIT: A Projected Clustering Technique -- Multi-Label Classification with a Constrained Minimum Cut Model -- On the Selection of Dimension Reduction Techniques for Scientific Applications -- Relearning Process for SPRT In Structural Change Detection of Time-Series Data -- K-means clustering on a classifier-induced representation space: application to customer contact personalization -- Dimensionality Reduction using Graph Weighted Subspace Learning for Bankruptcy Prediction -- Click Fraud Detection: Adversarial Pattern Recognition over 5 years at Microsoft -- A Novel Approach for Analysis of 'Real World' Data: A Data Mining Engine for Identification of Multi-author Student Document Submission -- Data Mining Based Tax Audit Selection: A Case Study of a Pilot Project at the Minnesota Department of Revenue -- A nearest neighbor approach to build a readable risk score for breast cancer -- Machine Learning for Medical Examination Report Processing -- Data Mining Vortex Cores Concurrent with Computational Fluid Dynamics Simulations -- A Data Mining Based Method for Discovery of Web Services and their Compositions -- Exploiting Terrain Information for Enhancing Fuel Economy of Cruising Vehicles by Supervised Training of Recurrent Neural Optimizers -- Exploration of Flight State and Control System Parameters for Prediction of Helicopter Loads via Gamma Test and Machine Learning Techniques -- Multilayer Semantic Analysis In Image Databases En línea: http://dx.doi.org/10.1007/978-3-319-07812-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35427 Real World Data Mining Applications [documento electrónico] / SpringerLink (Online service) ; Mahmoud Abou-Nasr ; Stefan Lessmann ; Stahlbock, Robert ; Gary M. Weiss . - Cham : Springer International Publishing : Imprint: Springer, 2015 . - XVI, 418 p. 144 illus., 96 illus. in color : online resource. - (Annals of Information Systems, ISSN 1934-3221; 17) .
ISBN : 978-3-319-07812-0
Idioma : Inglés (eng)
Palabras clave: Business Operations research Decision making Information technology Data processing mining and Management IT in Operation Research/Decision Theory Mining Knowledge Discovery Clasificación: 658 Empresas. Organización de empresas Resumen: Introduction Mahmoud Abou-Nasr, Stefan Lessmann. Robert Stahlbock, Gary M. Weiss What Data Scientists can Learn from History Aaron Lai On Line Mining of Cyclic Association Rules From Parallel Dimension Hierarchies Eya Ben Ahmed, Ahlem Nabli, Faiez Gargouri PROFIT: A Projected Clustering Technique Dharmveer Singh Rajput, Pramod Kumar Singh, Mahua Bhattacharya Multi-Label Classification with a Constrained Minimum Cut Model Guangzhi Qu, Ishwar Sethi, Craig Hartrick, Hui Zhang On the Selection of Dimension Reduction Techniques for Scientific Applications Ya Ju Fan, Chandrika Kamath Relearning Process for SPRT in Structural Change Detection of Time-Series Data Ryosuke Saga, Naoki Kaisaku, Hiroshi Tsuji K-means clustering on a classifier-induced representation space: application to customer contact personalization Vincent Lemaire, Fabrice Clerot, Nicolas Creff Dimensionality Reduction using Graph Weighted Subspace Learning for Bankruptcy Prediction Bernardete Ribeiro, Ning Chen Click Fraud Detection: Adversarial Pattern Recognition over 5 Years at Microsoft Brendan Kitts, Jing Ying Zhang, Gang Wu, Wesley Brandi, Julien Beasley, Kieran Morrill, John Ettedgui, Sid Siddhartha, Hong Yuan, Feng Gao, Peter Azo, Raj Mahato A Novel Approach for Analysis of ’Real World’ Data: A Data Mining Engine for Identification of Multi-author Student Document Submission Kathryn Burn-Thornton, Tim Burman Data Mining Based Tax Audit Selection: A Case Study of a Pilot Project at the Minnesota Department of Revenue Kuo-Wei Hsu, Nishith Pathak, Jaideep Srivastava, Greg Tschida, Eric Bjorklund A nearest neighbor approach to build a readable risk score for breast cancer Emilien Gauthier, Laurent Brisson, Philippe Lenca, Stephane Ragusa Machine Learning for Medical Examination Report Processing Yinghao Huang, Yi Lu Murphey, Naeem Seliya, Roy B. Friedenthal Data Mining Vortex Cores Concurrent with Computational Fluid Dynamics Simulations Clifton Mortensen, Steve Gorrell, Robert Woodley, Michael Gosnell A Data Mining Based Method for Discovery of Web Services and their Compositions Richi Nayak, Aishwarya Bose Exploiting Terrain Information for Enhancing Fuel Economy of Cruising Vehicles by Supervised Training of Recurrent Neural Optimizers Mahmoud Abou-Nasr, John Michelini, Dimitar Filev Exploration of Flight State and Control System Parameters for Prediction of Helicopter Loads via Gamma Test and Machine Learning Techniques Catherine Cheung, Julio J. Valdes, Matthew Li Multilayer Semantic Analysis In Image Databases Ismail El Sayad, Jean Martinet, Zhongfei (Mark) Zhang, Peter Eisert Nota de contenido: Introduction -- What Data Scientists can Learn from History -- On Line Mining of Cyclic Association Rules From Parallel Dimension Hierarchies -- PROFIT: A Projected Clustering Technique -- Multi-Label Classification with a Constrained Minimum Cut Model -- On the Selection of Dimension Reduction Techniques for Scientific Applications -- Relearning Process for SPRT In Structural Change Detection of Time-Series Data -- K-means clustering on a classifier-induced representation space: application to customer contact personalization -- Dimensionality Reduction using Graph Weighted Subspace Learning for Bankruptcy Prediction -- Click Fraud Detection: Adversarial Pattern Recognition over 5 years at Microsoft -- A Novel Approach for Analysis of 'Real World' Data: A Data Mining Engine for Identification of Multi-author Student Document Submission -- Data Mining Based Tax Audit Selection: A Case Study of a Pilot Project at the Minnesota Department of Revenue -- A nearest neighbor approach to build a readable risk score for breast cancer -- Machine Learning for Medical Examination Report Processing -- Data Mining Vortex Cores Concurrent with Computational Fluid Dynamics Simulations -- A Data Mining Based Method for Discovery of Web Services and their Compositions -- Exploiting Terrain Information for Enhancing Fuel Economy of Cruising Vehicles by Supervised Training of Recurrent Neural Optimizers -- Exploration of Flight State and Control System Parameters for Prediction of Helicopter Loads via Gamma Test and Machine Learning Techniques -- Multilayer Semantic Analysis In Image Databases En línea: http://dx.doi.org/10.1007/978-3-319-07812-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35427 Ejemplares
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Título : Algorithms from and for Nature and Life : Classification and Data Analysis Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Berthold Lausen ; Dirk van den Poel ; Alfred Ultsch Editorial: Cham : Springer International Publishing Fecha de publicación: 2013 Otro editor: Imprint: Springer Colección: Studies in Classification, Data Analysis, and Knowledge Organization, ISSN 1431-8814 Número de páginas: XX, 547 p. 111 illus Il.: online resource ISBN/ISSN/DL: 978-3-319-00035-0 Idioma : Inglés (eng) Palabras clave: Statistics Operations research Decision making Data mining Application software Management science and Computing/Statistics Programs Computer Appl. in Social Behavioral Sciences Operation Research/Decision Theory Research, Science Mining Knowledge Discovery for Business/Economics/Mathematical Finance/Insurance Clasificación: 51 Matemáticas Resumen: This volume provides approaches and solutions to challenges occurring at the interface of research fields such as, e.g., data analysis, data mining and knowledge discovery, computer science, operations research, and statistics. In addition to theory-oriented contributions various application areas are included. Moreover, traditional classification research directions concerning network data, graphs, and social relationships as well as statistical musicology describe examples for current interest fields tackled by the authors. The book comprises a total of 55 selected papers presented at the Joint Conference of the German Classification Society (GfKl), the German Association for Pattern Recognition (DAGM), and the Symposium of the International Federation of Classification Societies (IFCS) in 2011 Nota de contenido: Invited -- Clustering and Unsupervised Learning -- Statistical Data Analysis, Visualization and Scaling -- Bioinformatics and Biostatistics -- Archaeology and Geography, Psychology and Educational Sciences -- Text Mining, Social Networks and Clustering -- Banking and Finance -- Marketing and Management -- Music Classification Workshop En línea: http://dx.doi.org/10.1007/978-3-319-00035-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32450 Algorithms from and for Nature and Life : Classification and Data Analysis [documento electrónico] / SpringerLink (Online service) ; Berthold Lausen ; Dirk van den Poel ; Alfred Ultsch . - Cham : Springer International Publishing : Imprint: Springer, 2013 . - XX, 547 p. 111 illus : online resource. - (Studies in Classification, Data Analysis, and Knowledge Organization, ISSN 1431-8814) .
ISBN : 978-3-319-00035-0
Idioma : Inglés (eng)
Palabras clave: Statistics Operations research Decision making Data mining Application software Management science and Computing/Statistics Programs Computer Appl. in Social Behavioral Sciences Operation Research/Decision Theory Research, Science Mining Knowledge Discovery for Business/Economics/Mathematical Finance/Insurance Clasificación: 51 Matemáticas Resumen: This volume provides approaches and solutions to challenges occurring at the interface of research fields such as, e.g., data analysis, data mining and knowledge discovery, computer science, operations research, and statistics. In addition to theory-oriented contributions various application areas are included. Moreover, traditional classification research directions concerning network data, graphs, and social relationships as well as statistical musicology describe examples for current interest fields tackled by the authors. The book comprises a total of 55 selected papers presented at the Joint Conference of the German Classification Society (GfKl), the German Association for Pattern Recognition (DAGM), and the Symposium of the International Federation of Classification Societies (IFCS) in 2011 Nota de contenido: Invited -- Clustering and Unsupervised Learning -- Statistical Data Analysis, Visualization and Scaling -- Bioinformatics and Biostatistics -- Archaeology and Geography, Psychology and Educational Sciences -- Text Mining, Social Networks and Clustering -- Banking and Finance -- Marketing and Management -- Music Classification Workshop En línea: http://dx.doi.org/10.1007/978-3-319-00035-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32450 Ejemplares
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