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Biblioteca

MARC Lista (tabellare)
ACM/IMS transactions on data science
ACM/IMS transactions on data science
Pubbl/distr/stampa New York, NY : , : Association for Computing Machinery, , [2020]-
Descrizione fisica 1 online resource
Disciplina 006
Soggetto topico Data mining - Statistical methods
Big data - Statistical methods
Quantitative research
Données volumineuses - Méthodes statistiques
Recherche quantitative
Soggetto genere / forma Periodicals.
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Altri titoli varianti TDS
Transactions on data science
ACM transactions on data sciencec
Record Nr. UNISA-996548964503316
New York, NY : , : Association for Computing Machinery, , [2020]-
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
ACM/IMS transactions on data science
ACM/IMS transactions on data science
Pubbl/distr/stampa New York, NY : , : Association for Computing Machinery, , [2020]-
Descrizione fisica 1 online resource
Disciplina 006
Soggetto topico Data mining - Statistical methods
Big data - Statistical methods
Quantitative research
Données volumineuses - Méthodes statistiques
Recherche quantitative
Soggetto genere / forma Periodicals.
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Altri titoli varianti TDS
Transactions on data science
ACM transactions on data sciencec
Record Nr. UNINA-9910412143603321
New York, NY : , : Association for Computing Machinery, , [2020]-
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data mining and data visualization / edited by C. R. Rao, E. J. Wegman, J. L. Solka
Data mining and data visualization / edited by C. R. Rao, E. J. Wegman, J. L. Solka
Pubbl/distr/stampa Amsterdam : Elsevier North Holland, 2005
Descrizione fisica xiv, 643 p. : ill. (some col.), maps ; 25 cm
Disciplina 005.74
Altri autori (Persone) Rao, Calyampudi Radhakrishna
Wegman, Edward J.
Solka, Jeffrey L.
Collana Handbook of statistics, 0169-7161 ; 24
Soggetto topico Data mining
Data mining - Statistical methods
ISBN 0444511415
Classificazione AMS 62-06
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISALENTO-991001662499707536
Amsterdam : Elsevier North Holland, 2005
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
Data mining methods for the content analyst [[electronic resource] ] : an introduction to the computational analysis of content / / Kalev Hannes Leetaru
Data mining methods for the content analyst [[electronic resource] ] : an introduction to the computational analysis of content / / Kalev Hannes Leetaru
Autore Leetaru Kalev
Pubbl/distr/stampa New York, : Routledge, 2012
Descrizione fisica 1 online resource (121 p.)
Disciplina 006.3/12
006.312
Collana Routledge communication series
Soggetto topico Data mining
Data mining - Statistical methods
Soggetto genere / forma Electronic books.
ISBN 0-203-14938-6
1-283-84361-7
1-136-51459-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto DATA MINING METHODS FOR THE CONTENT ANALYST An Introduction to the Computational Analysis of Content; Copyright; Contents; List of Tables and Figures; Acknowledgments; 1 Introduction; What Is Content Analysis?; Why Use Computerized Analysis Techniques?; Standalone Tools or Integrated Suites; Transitioning from Theory to Practice; Chapter in Summary; 2 Obtaining and Preparing Data; Collecting Data from Digital Text Repositories; Are the Data Meaningful?; Using Data in Unintended Ways; Analytical Resolution; Types of Data Sources; Finding Sources; Searching Text Collections
Sources of IncompletenessLicensing Restrictions and Content Blackouts; Measuring Viewership; Accuracy and Convenience Samples; Random Samples; Multimedia Content; Converting to Textual Format; Prosody; Example Data Sources; Patterns in Historical War Coverage; Competitive Intelligence; Global News Coverage; Downloading Content; Digital Content; Print Content; Preparing Content; Document Extraction; Cleaning; Post Filtering; Reforming/Reshaping; Content Proxy Extraction; Chapter in Summary; 3 Vocabulary Analysis; The Basics; Word Histograms; Readability Indexes; Normative Comparison
Non-word AnalysisColloquialisms: Abbreviations and Slang; Restricting the Analytical Window; Vocabulary Comparison and Evolution/Chronemics; Advanced Topics; Syllables, Rhyming, and "Sounds Like"; Gender and Language; Authorship Attribution; Word Morphology, Stemming, and Lemmatization; Chapter in Summary; 4 Correlation and Co-occurrence; Understanding Correlation; Computing Word Correlations; Directionality; Concordance; Co-occurrence and Search; Language Variation and Lexicons; Non-co-occurrence; Correlation with Metadata; Chapter in Summary; 5 Lexicons, Entity Extraction, and Geocoding
LexiconsLexicons and Categorization; Lexical Correlation; Lexicon Consistency Checks; Thesauri and Vocabulary Expanders; Named Entity Extraction; Lexicons and Processing; Applications; Geocoding, Gazetteers, and Spatial Analysis; Geocoding; Gazetteers and the Geocoding Process; Operating Under Uncertainty; Spatial Analysis; Chapter in Summary; 6 Topic Extraction; How Machines Process Text; Unstructured Text; Extracting Meaning from Text; Applications of Topic Extraction; Comparing/Clustering Documents; Automatic Summarization; Automatic Keyword Generation
Multilingual Analysis: Topic Extraction with Multiple LanguagesChapter in Summary; 7 Sentiment Analysis; Examining Emotions; Evolution; Evaluation; Analytical Resolution: Documents versus Objects; Hand-crafted versus Automatically Generated Lexicons; Other Sentiment Scales; Limitations; Measuring Language Rather Than Worldview; Chapter in Summary; 8 Similarity, Categorization and Clustering; Categorization; The Vector Space Model; Feature Selection; Feature Reduction; Learning Algorithm; Evaluating ATC Results; Benefi ts of ATC over Human Categorization; Limitations of ATC
Applications of ATC
Record Nr. UNINA-9910462683603321
Leetaru Kalev  
New York, : Routledge, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data mining methods for the content analyst [[electronic resource] ] : an introduction to the computational analysis of content / / Kalev Hannes Leetaru
Data mining methods for the content analyst [[electronic resource] ] : an introduction to the computational analysis of content / / Kalev Hannes Leetaru
Autore Leetaru Kalev
Pubbl/distr/stampa New York, : Routledge, 2012
Descrizione fisica 1 online resource (121 p.)
Disciplina 006.3/12
006.312
Collana Routledge communication series
Soggetto topico Data mining
Data mining - Statistical methods
ISBN 0-203-14938-6
1-283-84361-7
1-136-51459-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto DATA MINING METHODS FOR THE CONTENT ANALYST An Introduction to the Computational Analysis of Content; Copyright; Contents; List of Tables and Figures; Acknowledgments; 1 Introduction; What Is Content Analysis?; Why Use Computerized Analysis Techniques?; Standalone Tools or Integrated Suites; Transitioning from Theory to Practice; Chapter in Summary; 2 Obtaining and Preparing Data; Collecting Data from Digital Text Repositories; Are the Data Meaningful?; Using Data in Unintended Ways; Analytical Resolution; Types of Data Sources; Finding Sources; Searching Text Collections
Sources of IncompletenessLicensing Restrictions and Content Blackouts; Measuring Viewership; Accuracy and Convenience Samples; Random Samples; Multimedia Content; Converting to Textual Format; Prosody; Example Data Sources; Patterns in Historical War Coverage; Competitive Intelligence; Global News Coverage; Downloading Content; Digital Content; Print Content; Preparing Content; Document Extraction; Cleaning; Post Filtering; Reforming/Reshaping; Content Proxy Extraction; Chapter in Summary; 3 Vocabulary Analysis; The Basics; Word Histograms; Readability Indexes; Normative Comparison
Non-word AnalysisColloquialisms: Abbreviations and Slang; Restricting the Analytical Window; Vocabulary Comparison and Evolution/Chronemics; Advanced Topics; Syllables, Rhyming, and "Sounds Like"; Gender and Language; Authorship Attribution; Word Morphology, Stemming, and Lemmatization; Chapter in Summary; 4 Correlation and Co-occurrence; Understanding Correlation; Computing Word Correlations; Directionality; Concordance; Co-occurrence and Search; Language Variation and Lexicons; Non-co-occurrence; Correlation with Metadata; Chapter in Summary; 5 Lexicons, Entity Extraction, and Geocoding
LexiconsLexicons and Categorization; Lexical Correlation; Lexicon Consistency Checks; Thesauri and Vocabulary Expanders; Named Entity Extraction; Lexicons and Processing; Applications; Geocoding, Gazetteers, and Spatial Analysis; Geocoding; Gazetteers and the Geocoding Process; Operating Under Uncertainty; Spatial Analysis; Chapter in Summary; 6 Topic Extraction; How Machines Process Text; Unstructured Text; Extracting Meaning from Text; Applications of Topic Extraction; Comparing/Clustering Documents; Automatic Summarization; Automatic Keyword Generation
Multilingual Analysis: Topic Extraction with Multiple LanguagesChapter in Summary; 7 Sentiment Analysis; Examining Emotions; Evolution; Evaluation; Analytical Resolution: Documents versus Objects; Hand-crafted versus Automatically Generated Lexicons; Other Sentiment Scales; Limitations; Measuring Language Rather Than Worldview; Chapter in Summary; 8 Similarity, Categorization and Clustering; Categorization; The Vector Space Model; Feature Selection; Feature Reduction; Learning Algorithm; Evaluating ATC Results; Benefi ts of ATC over Human Categorization; Limitations of ATC
Applications of ATC
Record Nr. UNINA-9910786303103321
Leetaru Kalev  
New York, : Routledge, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data science : theory, analysis, and applications / / edited by Qurban A Memon, Shakeel Ahmed Khoja
Data science : theory, analysis, and applications / / edited by Qurban A Memon, Shakeel Ahmed Khoja
Pubbl/distr/stampa Boca Raton : , : CRC Press, , [2020]
Descrizione fisica 1 online resource (345 pages)
Disciplina 006.312
Soggetto topico Data mining - Statistical methods
ISBN 0-429-55882-1
0-429-26379-1
0-429-55435-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910793884903321
Boca Raton : , : CRC Press, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data science : theory, analysis, and applications / / edited by Qurban A Memon, Shakeel Ahmed Khoja
Data science : theory, analysis, and applications / / edited by Qurban A Memon, Shakeel Ahmed Khoja
Pubbl/distr/stampa Boca Raton : , : CRC Press, , [2020]
Descrizione fisica 1 online resource (345 pages)
Disciplina 006.312
Soggetto topico Data mining - Statistical methods
ISBN 0-429-55882-1
0-429-26379-1
0-429-55435-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910799926403321
Boca Raton : , : CRC Press, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Handbook of statistical analysis and data mining applications / / authors, Robert Nisbet, Gary Miner, Ken Yale ; guest authors of selected chapters, John Elder, Andy Peterson
Handbook of statistical analysis and data mining applications / / authors, Robert Nisbet, Gary Miner, Ken Yale ; guest authors of selected chapters, John Elder, Andy Peterson
Autore Nisbet Robert
Edizione [Second edition.]
Pubbl/distr/stampa London, England : , : Academic Press, , 2018
Descrizione fisica 1 online resource (824 pages) : illustrations (some color)
Disciplina 006.312
Soggetto topico Data mining - Statistical methods
ISBN 0-12-416645-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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.
Record Nr. UNINA-9910583039803321
Nisbet Robert  
London, England : , : Academic Press, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Handbook of statistical analysis and data mining applications [[electronic resource] /] / Robert Nisbet, John Elder, Gary Miner
Handbook of statistical analysis and data mining applications [[electronic resource] /] / Robert Nisbet, John Elder, Gary Miner
Autore Nisbet Robert
Pubbl/distr/stampa Amsterdam ; ; Boston, : Academic Press/Elsevier, c2009
Descrizione fisica 1 online resource (859 p.)
Disciplina 006.3/12 22
519.5
Altri autori (Persone) ElderJohn F (John Fletcher)
MinerGary
Soggetto topico Data mining - Statistical methods
Multivariate analysis
Soggetto genere / forma Electronic books.
ISBN 1-282-16831-2
9786612168314
0-08-091203-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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
Record Nr. UNINA-9910455538003321
Nisbet Robert  
Amsterdam ; ; Boston, : Academic Press/Elsevier, c2009
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Handbook of statistical analysis and data mining applications [[electronic resource] /] / Robert Nisbet, John Elder, Gary Miner
Handbook of statistical analysis and data mining applications [[electronic resource] /] / Robert Nisbet, John Elder, Gary Miner
Autore Nisbet Robert
Pubbl/distr/stampa Amsterdam ; ; Boston, : Academic Press/Elsevier, c2009
Descrizione fisica 1 online resource (859 p.)
Disciplina 006.3/12 22
519.5
Altri autori (Persone) ElderJohn F (John Fletcher)
MinerGary
Soggetto topico Data mining - Statistical methods
Multivariate analysis
ISBN 1-282-16831-2
9786612168314
0-08-091203-6
Classificazione 31.73
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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
Record Nr. UNINA-9910777944903321
Nisbet Robert  
Amsterdam ; ; Boston, : Academic Press/Elsevier, c2009
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui