Big data mining and complexity:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Los Angeles ; London ; New Delhi ; Singapore ; Washington DC ; Melbourne
Sage
[2021]
|
| Schriftenreihe: | The Sage quantitative research kit
11th volume |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Enthält Literaturverzeichnis Seite 195-206 und Index |
| Beschreibung: | xiv, 212 Seiten Illustrationen, Diagramme |
| ISBN: | 9781526423818 |
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Datensatz im Suchindex
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| adam_text | CONTENTS List of Figures About the Authors 1 Introduction The Joys of Travel Data Mining and Big Data Travel Part I: Thinking Critically and Complex Organisation of Part I Part II: The Tools and Techniques of Data Mining SAGE Quantitative Research Kit COMPLEX-ГТ and the SACS Toolkit The Airline Industry: A Case Study PART I THINKING CRITICALLY AND COMPLEX 2 The Failure of Quantitative Social Science 3 xiii xv 1 2 2 3 4 5 7 7 8 11 13 Quantitative Social Science, Then and Now The Three Phases of Science What You Should Have Learned in Statistics Class So, Why Didn t You Learn These Things? Changing the Social Life of Data Analysis 15 15 16 19 21 What Is Big Data? 23 Big Data as Information Society Big Data as Global Network Society TheSocio-CyberneticWebofBigData Big Data Databases The Failed Promise of Big Data 24 25 26 28 31 4 What Is Data Mining? A Bit of Data Mining History The Data Mining Process 33 34 35
viii BIG DATA MINING AND COMPLEXITY The Black Box of Data Mining Validity and Reliability The Limits of Normalised Probability Distributions Fitting Models to Data Data Mining s Various Tasks 36 38 39 40 40 5 The Complexity Turn 43 Mapping the Complexity Turn Data Mining and Big Data as Complexity Science Top Ten List About Complexity Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7 Number 8 Number 9 Number 10 44 46 46 47 47 47 47 48 49 49 50 50 50 PART II THE TOOLS AND TECHNIQUES OF DATA MINING 53 6 Case-Based Complexity: A Data Mining Vocabulary Case-Based Complexity COMPLEX-ГТ and the SACS Toolkit The Ontology of Big Data The Archaeology ofBig Data Ontologies The Formalisms of Case-Based Complexity What Is a Case? Two Definitions of a Vector Cataloguing and Grouping Case Profiles Mathematical Distance Between Cases Cataloguing Case Profiles Diversity of Case Profiles The Notion of Time t Profiles That Vary With Time Static Clustering in Time Dynamic Clustering of Trajectories 55 - 56 57 59 59 63 63 64 65 66 67 69 69 69 70 70
CONTENTS A Vector Field The State Space A Discrete Vector Field of Velocitiesof Trajectories Why Do We Need a Vector Field? 7 Classification and Clustering Top Ten Airlines Classification Versus Clustering Classification Schemes Decision Tree Induction Nearest Neighbour Classifier Artificial Neural Networks Support Vector Machines Clustering Hierarchical Clustering Methods Partitioning Methods Probability Density-Based Methods Soft Computing Methods 8 Machine Learning The Smart Airline Industry What Is Machine Intelligence? Machine Intelligence for Big Data Examples of Data Mining Methods Based on Machine Intelligence Artificial Neural Networks Genetic Algorithms and Evolutionary Computation Swarming Travel Routes Overview of Swarm Intelligence 9 Predictive Analytics and DataForecasting Predictive Analytics and Data Forecasting: A Very Brief History Overview of Techniques Bayesian Statistics Bayesian Parameter Estimation Bayesian Hypothesis Testing Decision Trees Neural Networks Regression (Linear, Non-Linear and Logistic) ix 71 71 72 75 79 80 80 81 82 83 83 84 84 85 86 87 87 91 92 93 93 94 95 100 100 101 105 106 107 107 108 110 112 112 112
x BIG DATA MINING AND COMPLEXITY 10 Longitudinal Analysis Time Matters The Complexities ofData The Limitations ofMethod Choosing the Right Method Growth Mixture Modelling Growth Curves Multiple Group Growth Curve Modelling Growth Mixture Modelling Explained Differential Equations and Dynamical Systems Theory Examples of Global-Temporal Dynamics Modelling Process Comparing and Contrasting Methods 11 Geospatial Modelling The Importance of Geospatial Modelling How Does Geospatial Modelling Work? Conceptual Framework Prerequisites Spatial Relationships Geospatial Modelling With Real Data Part 1: Collecting and Organising Geospatial Data Part 2: Modelling and Analysing Geospatial Data 12 Complex Network Analysis What Is a Network? What Are Some Commonly Used Descriptors of Networks? What Are Some Properties of Networks? Some Examples of Widely Used Network Models Key Methods Used to Study Networks Matrices and Relational Data Network Methods: A Basic Overview Modelling Network Dynamics 13 Textual and Visual Data Mining Thinking About Textual/Visual Data Mining A Bit of History Preprocessing Unstructured Data 115 116 118 119 119 120 120 121 121 123 124 126 127 131 132 134 135 135 136 137 137 138 149 151 152 152 156 159 160 161 163 171 173 173 175
CONTENTS The Tools of Textual Data Mining xi 176 Sentiment Analysis 178 Visualising Results 180 14Conclusion: Advancing a Complex Digital Social Science 183 Changing the Social Life of Data Analysis 184 What Does It Take to Model Complex Systems? 186 A Few Methodological Caveats 186 Toolkits Rather Than Tools Breaking Interdisciplinary Barriers 188 188 Cross-Pollination of Complex Modelling Problems Truly Developing New Methods 188 188 Keeping Minor Trends 189 Divide and Conquer Scales Modelling Complex Causality 189 189 Data Heterogeneity and Dependence 191 Glossary References 193 195 Index 207
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| adam_txt |
CONTENTS List of Figures About the Authors 1 Introduction The Joys of Travel Data Mining and Big Data Travel Part I: Thinking Critically and Complex Organisation of Part I Part II: The Tools and Techniques of Data Mining SAGE Quantitative Research Kit COMPLEX-ГТ and the SACS Toolkit The Airline Industry: A Case Study PART I THINKING CRITICALLY AND COMPLEX 2 The Failure of Quantitative Social Science 3 xiii xv 1 2 2 3 4 5 7 7 8 11 13 Quantitative Social Science, Then and Now The Three Phases of Science What You Should Have Learned in Statistics Class So, Why Didn't You Learn These Things? Changing the Social Life of Data Analysis 15 15 16 19 21 What Is Big Data? 23 Big Data as Information Society Big Data as Global Network Society TheSocio-CyberneticWebofBigData Big Data Databases The Failed Promise of Big Data 24 25 26 28 31 4 What Is Data Mining? A Bit of Data Mining History The Data Mining Process 33 34 35
viii BIG DATA MINING AND COMPLEXITY The 'Black Box' of Data Mining Validity and Reliability The Limits of Normalised Probability Distributions Fitting Models to Data Data Mining's Various Tasks 36 38 39 40 40 5 The Complexity Turn 43 Mapping the Complexity Turn Data Mining and Big Data as Complexity Science Top Ten List About Complexity Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7 Number 8 Number 9 Number 10 44 46 46 47 47 47 47 48 49 49 50 50 50 PART II THE TOOLS AND TECHNIQUES OF DATA MINING 53 6 Case-Based Complexity: A Data Mining Vocabulary Case-Based Complexity COMPLEX-ГТ and the SACS Toolkit The Ontology of Big Data The Archaeology ofBig Data Ontologies The Formalisms of Case-Based Complexity What Is a Case? Two Definitions of a Vector Cataloguing and Grouping Case Profiles Mathematical Distance Between Cases Cataloguing Case Profiles Diversity of Case Profiles The Notion of Time t Profiles That Vary With Time Static Clustering in Time Dynamic Clustering of Trajectories 55 - 56 57 59 59 63 63 64 65 66 67 69 69 69 70 70
CONTENTS A Vector Field The State Space A Discrete Vector Field of Velocitiesof Trajectories Why Do We Need a Vector Field? 7 Classification and Clustering Top Ten Airlines Classification Versus Clustering Classification Schemes Decision Tree Induction Nearest Neighbour Classifier Artificial Neural Networks Support Vector Machines Clustering Hierarchical Clustering Methods Partitioning Methods Probability Density-Based Methods Soft Computing Methods 8 Machine Learning The Smart Airline Industry What Is Machine Intelligence? Machine Intelligence for Big Data Examples of Data Mining Methods Based on Machine Intelligence Artificial Neural Networks Genetic Algorithms and Evolutionary Computation Swarming Travel Routes Overview of Swarm Intelligence 9 Predictive Analytics and DataForecasting Predictive Analytics and Data Forecasting: A Very Brief History Overview of Techniques Bayesian Statistics Bayesian Parameter Estimation Bayesian Hypothesis Testing Decision Trees Neural Networks Regression (Linear, Non-Linear and Logistic) ix 71 71 72 75 79 80 80 81 82 83 83 84 84 85 86 87 87 91 92 93 93 94 95 100 100 101 105 106 107 107 108 110 112 112 112
x BIG DATA MINING AND COMPLEXITY 10 Longitudinal Analysis Time Matters The Complexities ofData The Limitations ofMethod Choosing the Right Method Growth Mixture Modelling Growth Curves Multiple Group Growth Curve Modelling Growth Mixture Modelling Explained Differential Equations and Dynamical Systems Theory Examples of Global-Temporal Dynamics Modelling Process Comparing and Contrasting Methods 11 Geospatial Modelling The Importance of Geospatial Modelling How Does Geospatial Modelling Work? Conceptual Framework Prerequisites Spatial Relationships Geospatial Modelling With Real Data Part 1: Collecting and Organising Geospatial Data Part 2: Modelling and Analysing Geospatial Data 12 Complex Network Analysis What Is a Network? What Are Some Commonly Used Descriptors of Networks? What Are Some Properties of Networks? Some Examples of Widely Used Network Models Key Methods Used to Study Networks Matrices and Relational Data Network Methods: A Basic Overview Modelling Network Dynamics 13 Textual and Visual Data Mining Thinking About Textual/Visual Data Mining A Bit of History Preprocessing Unstructured Data 115 116 118 119 119 120 120 121 121 123 124 126 127 131 132 134 135 135 136 137 137 138 149 151 152 152 156 159 160 161 163 171 173 173 175
CONTENTS The Tools of Textual Data Mining xi 176 Sentiment Analysis 178 Visualising Results 180 14Conclusion: Advancing a Complex Digital Social Science 183 Changing the Social Life of Data Analysis 184 What Does It Take to Model Complex Systems? 186 A Few Methodological Caveats 186 Toolkits Rather Than Tools Breaking Interdisciplinary Barriers 188 188 Cross-Pollination of Complex Modelling Problems Truly Developing New Methods 188 188 Keeping Minor Trends 189 Divide and Conquer Scales Modelling Complex Causality 189 189 Data Heterogeneity and Dependence 191 Glossary References 193 195 Index 207 |
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| series | The Sage quantitative research kit |
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| spelling | Castellani, Brian 1966- Verfasser (DE-588)173531946 aut Big data mining and complexity Brian C. Castellani, Rajeev Rajaram Los Angeles ; London ; New Delhi ; Singapore ; Washington DC ; Melbourne Sage [2021] xiv, 212 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier The Sage quantitative research kit 11th volume Enthält Literaturverzeichnis Seite 195-206 und Index Data Mining (DE-588)4428654-5 gnd rswk-swf Komplexität (DE-588)4135369-9 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf Big Data (DE-588)4802620-7 s Data Mining (DE-588)4428654-5 s Komplexität (DE-588)4135369-9 s DE-604 Rajaram, Rajeev Verfasser (DE-588)1246405326 aut The Sage quantitative research kit 11th volume (DE-604)BV047607953 11 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032995680&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Castellani, Brian 1966- Rajaram, Rajeev Big data mining and complexity The Sage quantitative research kit Data Mining (DE-588)4428654-5 gnd Komplexität (DE-588)4135369-9 gnd Big Data (DE-588)4802620-7 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4135369-9 (DE-588)4802620-7 |
| title | Big data mining and complexity |
| title_auth | Big data mining and complexity |
| title_exact_search | Big data mining and complexity |
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| title_full | Big data mining and complexity Brian C. Castellani, Rajeev Rajaram |
| title_fullStr | Big data mining and complexity Brian C. Castellani, Rajeev Rajaram |
| title_full_unstemmed | Big data mining and complexity Brian C. Castellani, Rajeev Rajaram |
| title_short | Big data mining and complexity |
| title_sort | big data mining and complexity |
| topic | Data Mining (DE-588)4428654-5 gnd Komplexität (DE-588)4135369-9 gnd Big Data (DE-588)4802620-7 gnd |
| topic_facet | Data Mining Komplexität Big Data |
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