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Event attendance prediction in social networks / / Xiaomei Zhang, Guohong Cao
| Event attendance prediction in social networks / / Xiaomei Zhang, Guohong Cao |
| Autore | Zhang Xiaomei |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (59 pages) |
| Disciplina | 004.65 |
| Collana | SpringerBriefs in statistics |
| Soggetto topico |
Mineria de dades
Comunitats virtuals Context-aware computing Data mining |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-89262-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466565603316 |
Zhang Xiaomei
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Event attendance prediction in social networks / / Xiaomei Zhang, Guohong Cao
| Event attendance prediction in social networks / / Xiaomei Zhang, Guohong Cao |
| Autore | Zhang Xiaomei |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (59 pages) |
| Disciplina | 004.65 |
| Collana | SpringerBriefs in statistics |
| Soggetto topico |
Mineria de dades
Comunitats virtuals Context-aware computing Data mining |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-89262-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910520081903321 |
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Zhang Xiaomei
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Fuzzy, rough and intuitionistic fuzzy set approaches for data handling : theory and applications / / edited by Tanmoy Som [and three others]
| Fuzzy, rough and intuitionistic fuzzy set approaches for data handling : theory and applications / / edited by Tanmoy Som [and three others] |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (279 pages) |
| Disciplina | 943.005 |
| Collana | Forum for Interdisciplinary Mathematics |
| Soggetto topico |
Data mining
Conjunts borrosos Mineria de dades Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9789811985669
9789811985652 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Fuzzy Sets and Rough Sets: A Mathematical Narrative -- Enhancing the Prediction of Anti-cancer Peptides by Suitable Feature Extraction and FRFS with ACO Search Followed by Resampling -- New Methods of Vagueness and Uncertainty Quantification in Lattice Boltzmann Method based Solute Transport Model -- Fuzzy Rough Set-based Feature Selection for Text Categorization -- An Extensive Survey on Detection of Malaria Parasites in Patients based on Fuzzy Approaches -- Application of Feature Extraction and Feature Selection followed by SMOTE to Improve the Prediction of DNA Binding Proteins -- Perspectives of Soft Computing in Multiscale Fluid Flow Systems -- Various Generalizations of Fuzzy Sets in the Context of Soft Computing and Decision Making -- A Linear Diophantine Fuzzy Soft Set based Decision Making Approach using Revised Max-Min Average Composition Method -- Recent Developments in Fuzzy Dynamic Data Envelopment Analysis and Its Applications -- Role of Centrality Measures in Link Prediction on Fuzzy Social Networks -- Interval Solutions of Fractional Integro-Differential Equations by using Modifed Adomian Decomposition Method -- Generalized Hukuhara Subdifferentiability for Convex Interval-valued Functions and its Applications in Nonsmooth Interval Optimization -- Rules Based Classifier for Identifying Fake Reviews in E-Commerce: Deep Learning System. |
| Record Nr. | UNINA-9910683348503321 |
| Singapore : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Heron streaming : fundamentals, applications, operations, and insights / / Huijun Wu, Maosong Fu
| Heron streaming : fundamentals, applications, operations, and insights / / Huijun Wu, Maosong Fu |
| Autore | Wu Huijun (Writer on cloud computing) |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (211 pages) : illustrations |
| Disciplina | 004.3 |
| Soggetto topico |
Information retrieval
Data mining Big data Information organization Recuperació de la informació Mineria de dades Dades massives |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-60094-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Who This Book Is For -- How This Book Is Organized -- What You Need for This Book -- Typographical Conventions -- Acknowledgments -- Contents -- About the Authors -- Part I Heron Fundamentals -- 1 Stream Processing -- 1.1 Big Data Processing -- 1.1.1 Lambda Architecture -- 1.1.1.1 Batch Processing Layer -- 1.1.1.2 Stream Processing Layer -- 1.1.1.3 Serving Layer -- 1.1.2 Kappa Architecture -- 1.2 Big Data Stream Processing -- 1.3 From Apache Storm to Apache Heron (Incubating) -- 1.3.1 Motivation for Heron -- 1.3.2 Heron Design Goals -- 1.3.3 Join the Apache Heron (Incubating) Community -- 1.4 Stream Processing Tools -- 1.5 Summary -- References -- 2 Heron Basics -- 2.1 Topology Data Model -- 2.1.1 Topology -- 2.1.2 Spout -- 2.1.3 Bolt -- 2.1.4 Grouping -- 2.2 Heron Architecture and Components -- 2.2.1 Cluster-Level Components (Six Components) -- 2.2.1.1 Scheduler -- 2.2.1.2 State Manager -- 2.2.1.3 Uploader -- 2.2.1.4 Heron CLI -- 2.2.1.5 Heron Tracker -- 2.2.1.6 Heron UI -- 2.2.2 Topology-Level Components (Four Components) -- 2.2.2.1 Heron Instance -- 2.2.2.2 Stream Manager -- 2.2.2.3 Topology Master -- 2.2.2.4 Metrics Manager -- 2.3 Submission Process and Failure Handling -- 2.4 Submit the First Topology -- 2.4.1 Preparation -- 2.4.2 Install the Heron Client -- 2.4.3 Heron Example Topologies -- 2.4.4 Submit the Topology JAR File -- 2.4.5 Observe the Running Topology -- 2.5 Summary -- References -- 3 Study Heron Code -- 3.1 Code Languages -- 3.2 Requirements for Compiling -- 3.3 Prepare the Compiling Environment -- 3.4 Source Organization -- 3.4.1 Directory Organization -- 3.4.2 Bazel Perspective -- 3.5 Compile Heron -- 3.6 Examine Compiling Results -- 3.6.1 Examine the API -- 3.6.2 Examine Packages -- 3.7 Run Tests -- 3.7.1 Unit Test -- 3.7.2 Integration Test -- 3.8 Summary -- References.
Part II Write Heron Topologies -- 4 Migrate Storm Topology to Heron -- 4.1 Prepare the Storm Topology Code -- 4.1.1 Examine the Storm Topology Code -- 4.1.2 Examine the Storm Flux Code -- 4.2 Migrate the Storm Topology Code to a HeronTopology Project -- 4.2.1 Adjust the Topology Java Code -- 4.2.2 Adjust the Project File pom.xml -- 4.2.2.1 Add Dependency -- 4.2.2.2 Build with Dependencies -- 4.2.3 Compile the Topology JAR File -- 4.3 Migrate Storm Flux to Heron ECO -- 4.4 Summary -- References -- 5 Write Topology Code -- 5.1 Before Writing Code -- 5.1.1 Design Topology -- 5.1.2 Choose a Heron API -- 5.2 Write Topology in Java -- 5.2.1 Code the Topology -- 5.2.1.1 Code Main -- 5.2.1.2 Code Spout -- 5.2.1.3 Code Bolt -- 5.2.2 Understand Tuple Flow -- 5.2.2.1 How Tuple Is Constructed -- 5.2.2.2 How Tuple Is Routed -- 5.3 Write Topology in Python -- 5.3.1 Code Main -- 5.3.2 Code Spout -- 5.3.3 Code Bolt -- 5.3.4 Compile and Run -- 5.4 Summary -- Reference -- 6 Heron Topology Features -- 6.1 Delivery Semantics -- 6.1.1 At-Least-Once -- 6.1.2 Effectively-Once -- 6.1.2.1 Requirements for Effectively-Once -- 6.1.2.2 Exactly-Once Versus Effectively-Once -- 6.1.2.3 Stateful Topologies -- 6.1.2.4 Implement Effectively-Once -- 6.2 Windowing -- 6.2.1 Windowing Concepts -- 6.2.2 Windowing Example -- 6.3 Summary -- Reference -- 7 Heron Streamlet API -- 7.1 Streamlet API Concepts -- 7.1.1 Streamlets -- 7.1.2 Operations -- 7.2 Write a Processing Graph with the Java Streamlet API -- 7.2.1 Sources -- 7.2.2 Sinks -- 7.2.3 Transform: Filter, Map, FlatMap -- 7.2.4 Partitioning -- 7.2.5 Clone and Union -- 7.2.6 Reduce by Key and Window -- 7.2.7 Join -- 7.2.8 Configuration -- 7.3 Write a Processing Graph with the Python Streamlet API -- 7.3.1 Source Generator -- 7.3.2 Processing Graph Construction -- 7.4 Write a Processing Graph with the Scala Streamlet API. 7.4.1 Install sbt -- 7.4.2 Source Directory -- 7.4.3 Compose Processing Graph -- 7.4.4 Examine the JAR File -- 7.5 Summary -- References -- Part III Operate Heron Clusters -- 8 Manage a Topology -- 8.1 Install Heron Client -- 8.1.1 What Is Inside heron-core.tar.gz -- 8.1.2 YAML Configuration -- 8.2 Run Topology -- 8.2.1 Topology Life Cycle -- 8.2.2 Submit Topology -- 8.2.3 Observe the Topology Running Status -- 8.3 Explore the ``heron'' Command -- 8.3.1 Common Arguments and Optional Flags -- 8.3.2 Explore ``heron submit'' Options -- 8.3.3 Kill Topology -- 8.3.4 Activate and Deactivate Topology -- 8.3.5 Restart Topology -- 8.3.6 Update Topology -- 8.4 Summary -- References -- 9 Manage Multiple Topologies -- 9.1 Install Heron Tools -- 9.2 Heron Tracker -- 9.3 Heron UI -- 9.4 Heron Explorer -- 9.5 Summary -- Reference -- Part IV Heron Insights -- 10 Explore Heron -- 10.1 Heron Processes -- 10.1.1 Java Processes -- 10.1.2 C++ Processes -- 10.1.3 Python Processes -- 10.2 State Manager -- 10.3 Heron Scheduler -- 10.3.1 Three Plans: Logical, Packing, and Physical -- 10.3.2 Restart Dead Processes -- 10.4 Data Flow -- 10.4.1 Capture Packets -- 10.4.2 Communication Primitive -- 10.5 Metrics System -- 10.5.1 File Sink -- 10.5.2 MetricsCache Manager Sink and TopologyMaster Sink -- 10.6 Summary -- 11 Extending the Heron Metrics Sink -- 11.1 Time Series -- 11.2 Metrics Category -- 11.3 Customize a Metrics Sink -- 11.3.1 Metrics Sink SPI -- 11.3.2 Metrics Sink Configuration -- 11.4 MySQL Metrics Sink -- 11.4.1 Implement IMetricsSink -- 11.4.2 Configure the Metrics Sink -- 11.4.3 Observe Metrics -- 11.5 Summary -- Reference -- 12 Extending Heron Scheduler -- 12.1 Scheduler SPI -- 12.1.1 ILauncher and IScheduler Work Together -- 12.1.2 ILauncher -- 12.1.3 IScheduler -- 12.2 Timeout Scheduler -- 12.2.1 Timeout Launcher -- 12.2.1.1 Prepare Container. 12.2.1.2 Launch by Service -- 12.2.1.3 Launch by Library -- 12.2.2 Timeout Scheduler -- 12.2.2.1 Abstract Parent Class -- 12.2.2.2 Service Mode -- 12.2.2.3 Library Mode -- 12.2.2.4 Service Mode Versus Library Mode -- 12.3 Summary -- 13 Heron Is Evolving -- 13.1 Dhalion and Health Manager -- 13.1.1 Dhalion -- 13.1.2 Health Manager -- 13.2 Deploy Mode (API Server) -- 13.3 Cloud-Native Heron -- 13.4 Summary -- Reference -- Index. |
| Record Nr. | UNINA-9910485028003321 |
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Wu Huijun (Writer on cloud computing)
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Heron streaming : fundamentals, applications, operations, and insights / / Huijun Wu, Maosong Fu
| Heron streaming : fundamentals, applications, operations, and insights / / Huijun Wu, Maosong Fu |
| Autore | Wu Huijun (Writer on cloud computing) |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (211 pages) : illustrations |
| Disciplina | 004.3 |
| Soggetto topico |
Information retrieval
Data mining Big data Information organization Recuperació de la informació Mineria de dades Dades massives |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-60094-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Who This Book Is For -- How This Book Is Organized -- What You Need for This Book -- Typographical Conventions -- Acknowledgments -- Contents -- About the Authors -- Part I Heron Fundamentals -- 1 Stream Processing -- 1.1 Big Data Processing -- 1.1.1 Lambda Architecture -- 1.1.1.1 Batch Processing Layer -- 1.1.1.2 Stream Processing Layer -- 1.1.1.3 Serving Layer -- 1.1.2 Kappa Architecture -- 1.2 Big Data Stream Processing -- 1.3 From Apache Storm to Apache Heron (Incubating) -- 1.3.1 Motivation for Heron -- 1.3.2 Heron Design Goals -- 1.3.3 Join the Apache Heron (Incubating) Community -- 1.4 Stream Processing Tools -- 1.5 Summary -- References -- 2 Heron Basics -- 2.1 Topology Data Model -- 2.1.1 Topology -- 2.1.2 Spout -- 2.1.3 Bolt -- 2.1.4 Grouping -- 2.2 Heron Architecture and Components -- 2.2.1 Cluster-Level Components (Six Components) -- 2.2.1.1 Scheduler -- 2.2.1.2 State Manager -- 2.2.1.3 Uploader -- 2.2.1.4 Heron CLI -- 2.2.1.5 Heron Tracker -- 2.2.1.6 Heron UI -- 2.2.2 Topology-Level Components (Four Components) -- 2.2.2.1 Heron Instance -- 2.2.2.2 Stream Manager -- 2.2.2.3 Topology Master -- 2.2.2.4 Metrics Manager -- 2.3 Submission Process and Failure Handling -- 2.4 Submit the First Topology -- 2.4.1 Preparation -- 2.4.2 Install the Heron Client -- 2.4.3 Heron Example Topologies -- 2.4.4 Submit the Topology JAR File -- 2.4.5 Observe the Running Topology -- 2.5 Summary -- References -- 3 Study Heron Code -- 3.1 Code Languages -- 3.2 Requirements for Compiling -- 3.3 Prepare the Compiling Environment -- 3.4 Source Organization -- 3.4.1 Directory Organization -- 3.4.2 Bazel Perspective -- 3.5 Compile Heron -- 3.6 Examine Compiling Results -- 3.6.1 Examine the API -- 3.6.2 Examine Packages -- 3.7 Run Tests -- 3.7.1 Unit Test -- 3.7.2 Integration Test -- 3.8 Summary -- References.
Part II Write Heron Topologies -- 4 Migrate Storm Topology to Heron -- 4.1 Prepare the Storm Topology Code -- 4.1.1 Examine the Storm Topology Code -- 4.1.2 Examine the Storm Flux Code -- 4.2 Migrate the Storm Topology Code to a HeronTopology Project -- 4.2.1 Adjust the Topology Java Code -- 4.2.2 Adjust the Project File pom.xml -- 4.2.2.1 Add Dependency -- 4.2.2.2 Build with Dependencies -- 4.2.3 Compile the Topology JAR File -- 4.3 Migrate Storm Flux to Heron ECO -- 4.4 Summary -- References -- 5 Write Topology Code -- 5.1 Before Writing Code -- 5.1.1 Design Topology -- 5.1.2 Choose a Heron API -- 5.2 Write Topology in Java -- 5.2.1 Code the Topology -- 5.2.1.1 Code Main -- 5.2.1.2 Code Spout -- 5.2.1.3 Code Bolt -- 5.2.2 Understand Tuple Flow -- 5.2.2.1 How Tuple Is Constructed -- 5.2.2.2 How Tuple Is Routed -- 5.3 Write Topology in Python -- 5.3.1 Code Main -- 5.3.2 Code Spout -- 5.3.3 Code Bolt -- 5.3.4 Compile and Run -- 5.4 Summary -- Reference -- 6 Heron Topology Features -- 6.1 Delivery Semantics -- 6.1.1 At-Least-Once -- 6.1.2 Effectively-Once -- 6.1.2.1 Requirements for Effectively-Once -- 6.1.2.2 Exactly-Once Versus Effectively-Once -- 6.1.2.3 Stateful Topologies -- 6.1.2.4 Implement Effectively-Once -- 6.2 Windowing -- 6.2.1 Windowing Concepts -- 6.2.2 Windowing Example -- 6.3 Summary -- Reference -- 7 Heron Streamlet API -- 7.1 Streamlet API Concepts -- 7.1.1 Streamlets -- 7.1.2 Operations -- 7.2 Write a Processing Graph with the Java Streamlet API -- 7.2.1 Sources -- 7.2.2 Sinks -- 7.2.3 Transform: Filter, Map, FlatMap -- 7.2.4 Partitioning -- 7.2.5 Clone and Union -- 7.2.6 Reduce by Key and Window -- 7.2.7 Join -- 7.2.8 Configuration -- 7.3 Write a Processing Graph with the Python Streamlet API -- 7.3.1 Source Generator -- 7.3.2 Processing Graph Construction -- 7.4 Write a Processing Graph with the Scala Streamlet API. 7.4.1 Install sbt -- 7.4.2 Source Directory -- 7.4.3 Compose Processing Graph -- 7.4.4 Examine the JAR File -- 7.5 Summary -- References -- Part III Operate Heron Clusters -- 8 Manage a Topology -- 8.1 Install Heron Client -- 8.1.1 What Is Inside heron-core.tar.gz -- 8.1.2 YAML Configuration -- 8.2 Run Topology -- 8.2.1 Topology Life Cycle -- 8.2.2 Submit Topology -- 8.2.3 Observe the Topology Running Status -- 8.3 Explore the ``heron'' Command -- 8.3.1 Common Arguments and Optional Flags -- 8.3.2 Explore ``heron submit'' Options -- 8.3.3 Kill Topology -- 8.3.4 Activate and Deactivate Topology -- 8.3.5 Restart Topology -- 8.3.6 Update Topology -- 8.4 Summary -- References -- 9 Manage Multiple Topologies -- 9.1 Install Heron Tools -- 9.2 Heron Tracker -- 9.3 Heron UI -- 9.4 Heron Explorer -- 9.5 Summary -- Reference -- Part IV Heron Insights -- 10 Explore Heron -- 10.1 Heron Processes -- 10.1.1 Java Processes -- 10.1.2 C++ Processes -- 10.1.3 Python Processes -- 10.2 State Manager -- 10.3 Heron Scheduler -- 10.3.1 Three Plans: Logical, Packing, and Physical -- 10.3.2 Restart Dead Processes -- 10.4 Data Flow -- 10.4.1 Capture Packets -- 10.4.2 Communication Primitive -- 10.5 Metrics System -- 10.5.1 File Sink -- 10.5.2 MetricsCache Manager Sink and TopologyMaster Sink -- 10.6 Summary -- 11 Extending the Heron Metrics Sink -- 11.1 Time Series -- 11.2 Metrics Category -- 11.3 Customize a Metrics Sink -- 11.3.1 Metrics Sink SPI -- 11.3.2 Metrics Sink Configuration -- 11.4 MySQL Metrics Sink -- 11.4.1 Implement IMetricsSink -- 11.4.2 Configure the Metrics Sink -- 11.4.3 Observe Metrics -- 11.5 Summary -- Reference -- 12 Extending Heron Scheduler -- 12.1 Scheduler SPI -- 12.1.1 ILauncher and IScheduler Work Together -- 12.1.2 ILauncher -- 12.1.3 IScheduler -- 12.2 Timeout Scheduler -- 12.2.1 Timeout Launcher -- 12.2.1.1 Prepare Container. 12.2.1.2 Launch by Service -- 12.2.1.3 Launch by Library -- 12.2.2 Timeout Scheduler -- 12.2.2.1 Abstract Parent Class -- 12.2.2.2 Service Mode -- 12.2.2.3 Library Mode -- 12.2.2.4 Service Mode Versus Library Mode -- 12.3 Summary -- 13 Heron Is Evolving -- 13.1 Dhalion and Health Manager -- 13.1.1 Dhalion -- 13.1.2 Health Manager -- 13.2 Deploy Mode (API Server) -- 13.3 Cloud-Native Heron -- 13.4 Summary -- Reference -- Index. |
| Record Nr. | UNISA-996466544503316 |
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Wu Huijun (Writer on cloud computing)
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Knowledge Management in Digital Change : New Findings and Practical Cases / / edited by Klaus North, Ronald Maier, Oliver Haas
| Knowledge Management in Digital Change : New Findings and Practical Cases / / edited by Klaus North, Ronald Maier, Oliver Haas |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (393 pages) : illustrations |
| Disciplina | 650 |
| Collana | Progress in IS |
| Soggetto topico |
Knowledge management
Data mining Business information services User interfaces (Computer systems) Human-computer interaction Electronic data processing - Management Industrial organization Knowledge Management Data Mining and Knowledge Discovery Business Information Systems User Interfaces and Human Computer Interaction IT Operations Organization Gestió del coneixement Gestió de la informació Mineria de dades Informàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-319-73546-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction: The Knowledge Ladder 4.0 - Value Creation in the Digitally Enabled Economy -- Part I: Digitally Enabled Enrichment of Resources to Leverage Human Performance -- Part II: Collaboration and Networking -- Part III: Leading and Learning 4.0 -- Part IV: New Forms of Knowledge-intensive Digitally Enabled Value Creation. . |
| Record Nr. | UNINA-9910298207903321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning for Data Science Handbook : Data Mining and Knowledge Discovery Handbook / / edited by Lior Rokach, Oded Maimon, Erez Shmueli
| Machine Learning for Data Science Handbook : Data Mining and Knowledge Discovery Handbook / / edited by Lior Rokach, Oded Maimon, Erez Shmueli |
| Autore | Rokach Lior |
| Edizione | [3rd ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (975 pages) |
| Disciplina | 006.312 |
| Altri autori (Persone) |
MaimonOded
ShmueliErez |
| Soggetto topico |
Machine learning
Artificial intelligence Data mining Information storage and retrieval systems Machine Learning Artificial Intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Mineria de dades Aprenentatge automàtic |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-24628-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to Knowledge Discovery and Data Mining -- Preprocessing Methods -- Data Cleansing: A Prelude to Knowledge Discovery -- Handling Missing Attribute Values -- Geometric Methods for Feature Extraction and Dimensional Reduction - A Guided Tour -- Dimension Reduction and Feature Selection -- Discretization Methods -- Outlier Detection -- Supervised Methods -- Supervised Learning -- Classification Trees -- Bayesian Networks -- Data Mining within a Regression Framework -- Support Vector Machines -- Rule Induction -- Unsupervised Methods -- A survey of Clustering Algorithms -- Association Rules -- Frequent Set Mining -- Constraint-based Data Mining -- Link Analysis -- Soft Computing Methods -- A Review of Evolutionary Algorithms for Data Mining -- A Review of Reinforcement Learning Methods -- Neural Networks For Data Mining -- Granular Computing and Rough Sets - An Incremental Development -- Pattern Clustering Using a Swarm Intelligence Approach -- Using Fuzzy Logic in Data Mining -- Supporting Methods -- Statistical Methods for Data Mining -- Logics for Data Mining -- Wavelet Methods in Data Mining -- Fractal Mining - Self Similarity-based Clustering and its Applications -- Visual Analysis of Sequences Using Fractal Geometry -- Interestingness Measures - On Determining What Is Interesting -- Quality Assessment Approaches in Data Mining -- Data Mining Model Comparison -- Data Mining Query Languages -- Advanced Methods -- Mining Multi-label Data -- Privacy in Data Mining -- Meta-Learning - Concepts and Techniques -- Bias vs Variance Decomposition for Regression and Classification -- Mining with Rare Cases -- Data Stream Mining -- Mining Concept-Drifting Data Streams -- Mining High-Dimensional Data -- Text Mining and Information Extraction -- Spatial Data Mining -- Spatio-temporal clustering -- Data Mining for Imbalanced Datasets: An Overview -- Relational Data Mining -- Web Mining -- A Review of Web Document Clustering Approaches -- Causal Discovery -- Ensemble Methods in Supervised Learning -- Data Mining using Decomposition Methods -- Information Fusion - Methods and Aggregation Operators -- Parallel and Grid-Based Data Mining – Algorithms, Models and Systems for High-Performance KDD -- Collaborative Data Mining -- Organizational Data Mining -- Mining Time Series Data -- Applications -- Multimedia Data Mining -- Data Mining in Medicine -- Learning Information Patterns in Biological Databases - Stochastic Data Mining -- Data Mining for Financial Applications -- Data Mining for Intrusion Detection -- Data Mining for CRM -- Data Mining for Target Marketing -- NHECD - Nano Health and Environmental Commented Database -- Software -- Commercial Data Mining Software -- Weka-A Machine Learning Workbench for Data Mining. |
| Record Nr. | UNINA-9910739470003321 |
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Rokach Lior
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Mathematical foundations for data analysis / / Jeff M. Phillips
| Mathematical foundations for data analysis / / Jeff M. Phillips |
| Autore | Phillips Jeff M. |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (299 pages) |
| Disciplina | 006.312 |
| Collana | Springer Series in the Data Sciences |
| Soggetto topico |
Data mining - Mathematics
Machine learning - Mathematics Mineria de dades Aprenentatge automàtic Matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-62341-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Contents -- 1 Probability Review -- 1.1 Sample Spaces -- 1.2 Conditional Probability and Independence -- 1.3 Density Functions -- 1.4 Expected Value -- 1.5 Variance -- 1.6 Joint, Marginal, and Conditional Distributions -- 1.7 Bayes' Rule -- 1.7.1 Model Given Data -- 1.8 Bayesian Inference -- Exercises -- 2 Convergence and Sampling -- 2.1 Sampling and Estimation -- 2.2 Probably Approximately Correct (PAC) -- 2.3 Concentration of Measure -- 2.3.1 Markov Inequality -- 2.3.2 Chebyshev Inequality -- 2.3.3 Chernoff-Hoeffding Inequality -- 2.3.4 Union Bound and Examples -- 2.4 Importance Sampling -- 2.4.1 Sampling Without Replacement with Priority Sampling -- Exercises -- 3 Linear Algebra Review -- 3.1 Vectors and Matrices -- 3.2 Addition and Multiplication -- 3.3 Norms -- 3.4 Linear Independence -- 3.5 Rank -- 3.6 Square Matrices and Properties -- 3.7 Orthogonality -- Exercises -- 4 Distances and Nearest Neighbors -- 4.1 Metrics -- 4.2 Lp Distances and their Relatives -- 4.2.1 Lp Distances -- 4.2.2 Mahalanobis Distance -- 4.2.3 Cosine and Angular Distance -- 4.2.4 KL Divergence -- 4.3 Distances for Sets and Strings -- 4.3.1 Jaccard Distance -- 4.3.2 Edit Distance -- 4.4 Modeling Text with Distances -- 4.4.1 Bag-of-Words Vectors -- 4.4.2 k-Grams -- 4.5 Similarities -- 4.5.1 Set Similarities -- 4.5.2 Normed Similarities -- 4.5.3 Normed Similarities between Sets -- 4.6 Locality Sensitive Hashing -- 4.6.1 Properties of Locality Sensitive Hashing -- 4.6.2 Prototypical Tasks for LSH -- 4.6.3 Banding to Amplify LSH -- 4.6.4 LSH for Angular Distance -- 4.6.5 LSH for Euclidean Distance -- 4.6.6 Min Hashing as LSH for Jaccard Distance -- Exercises -- 5 Linear Regression -- 5.1 Simple Linear Regression -- 5.2 Linear Regression with Multiple Explanatory Variables -- 5.3 Polynomial Regression -- 5.4 Cross-Validation.
5.4.1 Other ways to Evaluate Linear Regression Models -- 5.5 Regularized Regression -- 5.5.1 Tikhonov Regularization for Ridge Regression -- 5.5.2 Lasso -- 5.5.3 Dual Constrained Formulation -- 5.5.4 Matching Pursuit -- Exercises -- 6 Gradient Descent -- 6.1 Functions -- 6.2 Gradients -- 6.3 Gradient Descent -- 6.3.1 Learning Rate -- 6.4 Fitting a Model to Data -- 6.4.1 Least Mean Squares Updates for Regression -- 6.4.2 Decomposable Functions -- Exercises -- 7 Dimensionality Reduction -- 7.1 Data Matrices -- 7.1.1 Projections -- 7.1.2 Sum of Squared Errors Goal -- 7.2 Singular Value Decomposition -- 7.2.1 Best Rank-k Approximation of a Matrix -- 7.3 Eigenvalues and Eigenvectors -- 7.4 The Power Method -- 7.5 Principal Component Analysis -- 7.6 Multidimensional Scaling -- 7.6.1 Why does Classical MDS work? -- 7.7 Linear Discriminant Analysis -- 7.8 Distance Metric Learning -- 7.9 Matrix Completion -- 7.10 Random Projections -- Exercises -- 8 Clustering -- 8.1 Voronoi Diagrams -- 8.1.1 Delaunay Triangulation -- 8.1.2 Connection to Assignment-Based Clustering -- 8.2 Gonzalez's Algorithm for k-Center Clustering -- 8.3 Lloyd's Algorithm for k-Means Clustering -- 8.3.1 Lloyd's Algorithm -- 8.3.2 k-Means++ -- 8.3.3 k-Mediod Clustering -- 8.3.4 Soft Clustering -- 8.4 Mixture of Gaussians -- 8.4.1 Expectation-Maximization -- 8.5 Hierarchical Clustering -- 8.6 Density-Based Clustering and Outliers -- 8.6.1 Outliers -- 8.7 Mean Shift Clustering -- Exercises -- 9 Classification -- 9.1 Linear Classifiers -- 9.1.1 Loss Functions -- 9.1.2 Cross-Validation and Regularization -- 9.2 Perceptron Algorithm -- 9.3 Support Vector Machines and Kernels -- 9.3.1 The Dual: Mistake Counter -- 9.3.2 Feature Expansion -- 9.3.3 Support Vector Machines -- 9.4 Learnability and VC dimension -- 9.5 kNN Classifiers -- 9.6 Decision Trees -- 9.7 Neural Networks. 9.7.1 Training with Back-propagation -- 10 Graph Structured Data -- 10.1 Markov Chains -- 10.1.1 Ergodic Markov Chains -- 10.1.2 Metropolis Algorithm -- 10.2 PageRank -- 10.3 Spectral Clustering on Graphs -- 10.3.1 Laplacians and their EigenStructures -- 10.4 Communities in Graphs -- 10.4.1 Preferential Attachment -- 10.4.2 Betweenness -- 10.4.3 Modularity -- Exercises -- 11 Big Data and Sketching -- 11.1 The Streaming Model -- 11.1.1 Mean and Variance -- 11.1.2 Reservoir Sampling -- 11.2 Frequent Items -- 11.2.1 Warm-Up: Majority -- 11.2.2 Misra-Gries Algorithm -- 11.2.3 Count-Min Sketch -- 11.2.4 Count Sketch -- 11.3 Matrix Sketching -- 11.3.1 Covariance Matrix Summation -- 11.3.2 Frequent Directions -- 11.3.3 Row Sampling -- 11.3.4 Random Projections and Count Sketch Hashing -- Exercises -- Index. |
| Record Nr. | UNINA-9910483358803321 |
|
Phillips Jeff M.
|
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Mathematical foundations for data analysis / / Jeff M. Phillips
| Mathematical foundations for data analysis / / Jeff M. Phillips |
| Autore | Phillips Jeff M. |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (299 pages) |
| Disciplina | 006.312 |
| Collana | Springer Series in the Data Sciences |
| Soggetto topico |
Data mining - Mathematics
Machine learning - Mathematics Mineria de dades Aprenentatge automàtic Matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-62341-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Contents -- 1 Probability Review -- 1.1 Sample Spaces -- 1.2 Conditional Probability and Independence -- 1.3 Density Functions -- 1.4 Expected Value -- 1.5 Variance -- 1.6 Joint, Marginal, and Conditional Distributions -- 1.7 Bayes' Rule -- 1.7.1 Model Given Data -- 1.8 Bayesian Inference -- Exercises -- 2 Convergence and Sampling -- 2.1 Sampling and Estimation -- 2.2 Probably Approximately Correct (PAC) -- 2.3 Concentration of Measure -- 2.3.1 Markov Inequality -- 2.3.2 Chebyshev Inequality -- 2.3.3 Chernoff-Hoeffding Inequality -- 2.3.4 Union Bound and Examples -- 2.4 Importance Sampling -- 2.4.1 Sampling Without Replacement with Priority Sampling -- Exercises -- 3 Linear Algebra Review -- 3.1 Vectors and Matrices -- 3.2 Addition and Multiplication -- 3.3 Norms -- 3.4 Linear Independence -- 3.5 Rank -- 3.6 Square Matrices and Properties -- 3.7 Orthogonality -- Exercises -- 4 Distances and Nearest Neighbors -- 4.1 Metrics -- 4.2 Lp Distances and their Relatives -- 4.2.1 Lp Distances -- 4.2.2 Mahalanobis Distance -- 4.2.3 Cosine and Angular Distance -- 4.2.4 KL Divergence -- 4.3 Distances for Sets and Strings -- 4.3.1 Jaccard Distance -- 4.3.2 Edit Distance -- 4.4 Modeling Text with Distances -- 4.4.1 Bag-of-Words Vectors -- 4.4.2 k-Grams -- 4.5 Similarities -- 4.5.1 Set Similarities -- 4.5.2 Normed Similarities -- 4.5.3 Normed Similarities between Sets -- 4.6 Locality Sensitive Hashing -- 4.6.1 Properties of Locality Sensitive Hashing -- 4.6.2 Prototypical Tasks for LSH -- 4.6.3 Banding to Amplify LSH -- 4.6.4 LSH for Angular Distance -- 4.6.5 LSH for Euclidean Distance -- 4.6.6 Min Hashing as LSH for Jaccard Distance -- Exercises -- 5 Linear Regression -- 5.1 Simple Linear Regression -- 5.2 Linear Regression with Multiple Explanatory Variables -- 5.3 Polynomial Regression -- 5.4 Cross-Validation.
5.4.1 Other ways to Evaluate Linear Regression Models -- 5.5 Regularized Regression -- 5.5.1 Tikhonov Regularization for Ridge Regression -- 5.5.2 Lasso -- 5.5.3 Dual Constrained Formulation -- 5.5.4 Matching Pursuit -- Exercises -- 6 Gradient Descent -- 6.1 Functions -- 6.2 Gradients -- 6.3 Gradient Descent -- 6.3.1 Learning Rate -- 6.4 Fitting a Model to Data -- 6.4.1 Least Mean Squares Updates for Regression -- 6.4.2 Decomposable Functions -- Exercises -- 7 Dimensionality Reduction -- 7.1 Data Matrices -- 7.1.1 Projections -- 7.1.2 Sum of Squared Errors Goal -- 7.2 Singular Value Decomposition -- 7.2.1 Best Rank-k Approximation of a Matrix -- 7.3 Eigenvalues and Eigenvectors -- 7.4 The Power Method -- 7.5 Principal Component Analysis -- 7.6 Multidimensional Scaling -- 7.6.1 Why does Classical MDS work? -- 7.7 Linear Discriminant Analysis -- 7.8 Distance Metric Learning -- 7.9 Matrix Completion -- 7.10 Random Projections -- Exercises -- 8 Clustering -- 8.1 Voronoi Diagrams -- 8.1.1 Delaunay Triangulation -- 8.1.2 Connection to Assignment-Based Clustering -- 8.2 Gonzalez's Algorithm for k-Center Clustering -- 8.3 Lloyd's Algorithm for k-Means Clustering -- 8.3.1 Lloyd's Algorithm -- 8.3.2 k-Means++ -- 8.3.3 k-Mediod Clustering -- 8.3.4 Soft Clustering -- 8.4 Mixture of Gaussians -- 8.4.1 Expectation-Maximization -- 8.5 Hierarchical Clustering -- 8.6 Density-Based Clustering and Outliers -- 8.6.1 Outliers -- 8.7 Mean Shift Clustering -- Exercises -- 9 Classification -- 9.1 Linear Classifiers -- 9.1.1 Loss Functions -- 9.1.2 Cross-Validation and Regularization -- 9.2 Perceptron Algorithm -- 9.3 Support Vector Machines and Kernels -- 9.3.1 The Dual: Mistake Counter -- 9.3.2 Feature Expansion -- 9.3.3 Support Vector Machines -- 9.4 Learnability and VC dimension -- 9.5 kNN Classifiers -- 9.6 Decision Trees -- 9.7 Neural Networks. 9.7.1 Training with Back-propagation -- 10 Graph Structured Data -- 10.1 Markov Chains -- 10.1.1 Ergodic Markov Chains -- 10.1.2 Metropolis Algorithm -- 10.2 PageRank -- 10.3 Spectral Clustering on Graphs -- 10.3.1 Laplacians and their EigenStructures -- 10.4 Communities in Graphs -- 10.4.1 Preferential Attachment -- 10.4.2 Betweenness -- 10.4.3 Modularity -- Exercises -- 11 Big Data and Sketching -- 11.1 The Streaming Model -- 11.1.1 Mean and Variance -- 11.1.2 Reservoir Sampling -- 11.2 Frequent Items -- 11.2.1 Warm-Up: Majority -- 11.2.2 Misra-Gries Algorithm -- 11.2.3 Count-Min Sketch -- 11.2.4 Count Sketch -- 11.3 Matrix Sketching -- 11.3.1 Covariance Matrix Summation -- 11.3.2 Frequent Directions -- 11.3.3 Row Sampling -- 11.3.4 Random Projections and Count Sketch Hashing -- Exercises -- Index. |
| Record Nr. | UNISA-996466554403316 |
|
Phillips Jeff M.
|
||
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Recommender systems in fashion and retail / / Editors, Nima Dokoohak [and three others]
| Recommender systems in fashion and retail / / Editors, Nima Dokoohak [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (160 pages) : illustrations |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Electrical Engineering |
| Soggetto topico |
Computational intelligence
Artificial intelligence Data mining Intel·ligència computacional Intel·ligència artificial Mineria de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-66103-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466548803316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Soggetto topico
-
Mineria de dades
(44)
- Data mining (26)
- Big data (12)
- Artificial intelligence (8)
- Dades massives (8)