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  • 1
    UID:
    almahu_BV035483378
    Format: XXXIV, 824 S. : , Ill., graph. Darst. , DVD (12 cm)
    ISBN: 978-0-12-374765-5 , 0-12-374765-1
    Language: English
    Subjects: Computer Science , Economics , Mathematics
    RVK:
    RVK:
    RVK:
    Keywords: Data Mining ; Statistik
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Online Resource
    Online Resource
    Amsterdam [u.a.] :Acad. Press,
    UID:
    almahu_BV042310453
    Format: 1 Online-Ressource (XL, 1053 S.) : , Ill., graph. Darst.
    Edition: 1. ed
    ISBN: 978-1-283-39625-7 , 978-0-12-387011-7
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-386979-1
    Language: English
    Subjects: Computer Science , Mathematics
    RVK:
    RVK:
    Keywords: Text Mining
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 3
    UID:
    almahu_BV039834055
    Format: 1 Online-Ressource (XXXIX, 824 Seiten) : , Illustrationen, Grafiken.
    ISBN: 978-0-12-374765-5
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-374765-5
    Language: English
    Subjects: Computer Science , Economics , Mathematics
    RVK:
    RVK:
    RVK:
    Keywords: Statistik ; Data Mining
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 4
    Online Resource
    Online Resource
    Amsterdam ; : Academic Press/Elsevier,
    UID:
    almahu_9947366709402882
    Format: 1 online resource (859 pages)
    Edition: First edition.
    ISBN: 1-282-16831-2 , 9786612168314 , 0-08-091203-6
    Content: The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be a
    Note: Description based upon print version of record. , Front Cover; Handbook of Statistical Analysis and Data Mining Applications; Copyright Page; Table of Contents; Foreword 1; Foreword 2; Preface; Introduction; List of Tutorials by Guest Authors; Part 1: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process; Chapter 1: The Background for Data Mining Practice; Assumptions of the Parametric Model; Two Views of Reality; Aristotle; Plato; The Rise of Modern Statistical Analysis: The Second Generation; Machine Learning Methods: The Third Generation; Statistical Learning Theory: The Fourth Generation , Chapter 2: Theoretical Considerations for Data MiningMajor Issues in Data Mining; General Requirements for Success in a Data Mining Project; The Importance of Domain Knowledge; Postscript; Some Caveats with Data Mining Solutions; Chapter 3: The Data Mining Process; CRISP-DM; Assess the Business Environment for Data Mining; Data Understanding (Mostly Science); References; Preamble; Chapter 4: Data Understanding and Preparation; Preamble; Issues That Should be Resolved; Splitting Data , Part 1: Using a Wrapper Approach in Weka to Determine the Most Appropriate Variables for Your Neural Network ModelExample 4; Data Extraction; Data Weighting and Balancing; Data Filtering and Smoothing; Data Abstraction; Data Reduction; Data Sampling; Data Discretization; Data Derivation; Postscript; Chapter 5: Feature Selection; Inductive Database Approach; Bi-variate Methods; Multivariate Methods; Postscript; Complex Methods; The Other Two Ways of Using Feature Selection in STATISTICA: Interactive Workspace; Preamble; Chapter 6: Accessory Tools for Doing Data Mining; Preamble; Introduction , Basic Descriptive StatisticsCombining Groups (Classes) for Predictive Data Mining; Generalized Linear Models (GLMs); Data Miner Workspace Templates; Comparison of Models with and Without Time-Based Features; Example: The IDP Facility of STATISTICA Data Miner; Ensembles in General; Part 2: The Algorithms in Data Mining and Text Mining, the Organization of the Three most common Data Mining Tools, and Selected Speci...; Chapter 7: Basic Algorithms for Data Mining: A Brief Overview; Preamble; STATISTICA Data Miner Recipe (DMRecipe); Automated Neural Nets; Generalized Additive Models (GAMs) , Outputs of GAMsRecursive Partitioning; Pruning Trees; Bibliography; Chapter 8: Advanced Algorithms for Data Mining; The Physical Data Mart; Summary; Micro-Target the Profitable Customers; Quality Control Data Mining and Root Cause Analysis; Chapter 9: Text Mining and Natural Language Processing; The Development of Text Mining; Chapter 10: The Three Most Common Data Mining Software Tools; Preamble; SPSS Clementine Overview; Preamble; Setting the Default Directory; Visual Data Preparation for Data Mining: Taking Photos, Moving Pictures, and Objects into Spreadsheets Representing the Photos... , Preamble , English
    Additional Edition: ISBN 0-12-374765-1
    Language: English
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  • 5
    UID:
    almahu_9948078151002882
    Format: 1 online resource (824 pages) : , illustrations (some color)
    Edition: Second edition.
    ISBN: 0-12-416645-8
    Content: Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas-from science and engineering, to medicine, academia and commerce.
    Note: History of phases of data analysis, basic theory, and the data mining process -- The algorithms and methods in data mining and predictive analytics and some domain areas -- Tutorials and case studies -- Models ensembles, model complexity; using the right model for the right use, significance, ethics, and the future and advanced processes.
    Additional Edition: ISBN 0-12-416632-6
    Language: English
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  • 6
    UID:
    almahu_9949455313802882
    Format: 1 online resource (578 pages)
    Edition: Second edition.
    ISBN: 0-323-95275-5
    Additional Edition: Print version: Miner, Gary D. Practical Data Analytics for Innovation in Medicine San Diego : Elsevier Science & Technology,c2023 ISBN 9780323952743
    Language: English
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  • 7
    UID:
    almahu_9949697785502882
    Format: 1 online resource (1095 pages)
    Edition: First edition.
    ISBN: 1-283-39625-4 , 9786613396259 , 0-12-387011-9
    Content: "The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. This comprehensive professional reference brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. The Handbook of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications presents a comprehensive how- to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities"--
    Note: Description based upon print version of record , Machine generated contents note: Preface: What is TM and what it can do for you Introduction: How to use this book, and chapter summaries Part I: History, Process and Applications of Text Mining; Part II: Tutorials Part III: Areas of Technical Focus in Text Mining Part V: Text Mining Practice and Prospect: The Right Model for the Right Purpose, Summary, and the Future of TM. , English
    Additional Edition: ISBN 0-12-386979-X
    Language: English
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  • 8
    UID:
    almahu_BV040527543
    Format: XL, 1053 S. : , Ill., graph. Darst. , 1 DVD-ROM (12 cm)
    Edition: 1. ed.
    ISBN: 978-0-12-386979-1
    Language: English
    Keywords: Text Mining
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  • 9
    Online Resource
    Online Resource
    London :Academic Press, an imprint of Elsevier,
    UID:
    edoccha_BV045382641
    Format: 1 Online-Ressource (xxix, 792 Seiten) : , Illustrationen.
    Edition: Second edition
    ISBN: 978-0-12-416645-5
    Content: Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas-from science and engineering, to medicine, academia and commerce
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-416632-5
    Language: English
    Subjects: Computer Science , Economics , Mathematics
    RVK:
    RVK:
    RVK:
    Keywords: Data Mining ; Statistik
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
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  • 10
    Online Resource
    Online Resource
    London :Academic Press, an imprint of Elsevier,
    UID:
    almafu_BV045382641
    Format: 1 Online-Ressource (xxix, 792 Seiten) : , Illustrationen.
    Edition: Second edition
    ISBN: 978-0-12-416645-5
    Content: Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas-from science and engineering, to medicine, academia and commerce
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-416632-5
    Language: English
    Subjects: Computer Science , Economics , Mathematics
    RVK:
    RVK:
    RVK:
    Keywords: Data Mining ; Statistik
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
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