Coverart for item
The Resource Adaptive resonance theory in social media data clustering : roles, methodologies, and applications, Lei Meng, Ah-Hwee Tan and Donald C. Wunsch II

Adaptive resonance theory in social media data clustering : roles, methodologies, and applications, Lei Meng, Ah-Hwee Tan and Donald C. Wunsch II

Label
Adaptive resonance theory in social media data clustering : roles, methodologies, and applications
Title
Adaptive resonance theory in social media data clustering
Title remainder
roles, methodologies, and applications
Statement of responsibility
Lei Meng, Ah-Hwee Tan and Donald C. Wunsch II
Creator
Contributor
Author
Subject
Language
eng
Summary
Social media data contains our communication and online sharing, mirroring our daily life. This book looks at how we can use and what we can discover from such big data:Basic knowledge (data & challenges) on social media analyticsClustering as a fundamental technique for unsupervised knowledge discovery and data miningA class of neural inspired algorithms, based on adaptive resonance theory (ART), tackling challenges in big social media data clustering Step-by-step practices of developing unsupervised machine learning algorithms for real-world applications in social media domainAdaptive Resonance Theory in Social Media Data Clustering stands on the fundamental breakthrough in cognitive and neural theory, i.e. adaptive resonance theory, which simulates how a brain processes information to perform memory, learning, recognition, and prediction.It presents initiatives on the mathematical demonstration of ART{u2019}s learning mechanisms in clustering, and illustrates how to extend the base ART model to handle the complexity and characteristics of social media data and perform associative analytical tasks.Both cutting-edge research and real-world practices on machine learning and social media analytics are included in the book and if you wish to learn the answers to the following questions, this book is for you:How to process big streams of multimedia data?How to analyze social networks with heterogeneous data?How to understand a user{u2019}s interests by learning from online posts and behaviors?How to create a personalized search engine by automatically indexing and searching multimodal information resources?          
Member of
Cataloging source
N$T
http://library.link/vocab/creatorName
Meng, Lei
Dewey number
005.7
Index
index present
LC call number
QA76.9.B45
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Tan, Ah-Hwee
  • Wunsch, Donald C.
Series statement
Advanced information and knowledge processing
http://library.link/vocab/subjectName
  • Big data.
  • Data mining.
  • Social media.
Label
Adaptive resonance theory in social media data clustering : roles, methodologies, and applications, Lei Meng, Ah-Hwee Tan and Donald C. Wunsch II
Instantiates
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; Preface; Scope; Content; Audience; Acknowledgments; Contents; Theories; 1 Introduction; 1.1 Clustering in the Era of Web 2.0; 1.2 Research Issues and Challenges; 1.2.1 Representation of Social Media Data; 1.2.2 Scalability for Big Data; 1.2.3 Robustness to Noisy Features; 1.2.4 Heterogeneous Information Fusion; 1.2.5 Sensitivity to Input Parameters; 1.2.6 Online Learning Capability; 1.2.7 Incorporation of User Preferences; 1.3 Approach and Methodology; 1.4 Outline of the Book; References; 2 Clustering and Its Extensions in the Social Media Domain; 2.1 Clustering
  • 2.1.1 K-Means Clustering2.1.2 Hierarchical Clustering; 2.1.3 Graph Theoretic Clustering; 2.1.4 Latent Semantic Analysis; 2.1.5 Non-Negative Matrix Factorization; 2.1.6 Probabilistic Clustering; 2.1.7 Genetic Clustering; 2.1.8 Density-Based Clustering; 2.1.9 Affinity Propagation; 2.1.10 Clustering by Finding Density Peaks; 2.1.11 Adaptive Resonance Theory; 2.2 Semi-Supervised Clustering; 2.2.1 Group Label Constraint; 2.2.2 Pairwise Label Constraint; 2.3 Heterogeneous Data Co-Clustering; 2.3.1 Graph Theoretic Models; 2.3.2 Non-Negative Matrix Factorization Models
  • 2.3.3 Markov Random Field Model2.3.4 Multi-view Clustering Models; 2.3.5 Aggregation-Based Models; 2.3.6 Fusion Adaptive Resonance Theory; 2.4 Online Clustering; 2.4.1 Incremental Learning Strategies; 2.4.2 Online Learning Strategies; 2.5 Automated Data Cluster Recognition; 2.5.1 Cluster Tendency Analysis; 2.5.2 Posterior Cluster Validation Approach; 2.5.3 Algorithms Without a Pre-defined Number of Clusters; 2.6 Social Media Mining and Related Clustering Techniques; 2.6.1 Web Image Organization; 2.6.2 Multimodal Social Information Fusion; 2.6.3 User Community Detection in Social Networks
  • 2.6.4 User Sentiment Analysis2.6.5 Event Detection in Social Networks; 2.6.6 Community Question Answering; 2.6.7 Social Media Data Indexing and Retrieval; 2.6.8 Multifaceted Recommendation in Social Networks; References; 3 Adaptive Resonance Theory (ART) for Social Media Analytics; 3.1 Fuzzy ART; 3.1.1 Clustering Algorithm of Fuzzy ART; 3.1.2 Algorithm Analysis; 3.2 Geometric Interpretation of Fuzzy ART; 3.2.1 Complement Coding in Fuzzy ART; 3.2.2 Vigilance Region (VR); 3.2.3 Modeling Clustering Dynamics of Fuzzy ART Using VRs; 3.2.4 Discussion
  • 3.3 Vigilance Adaptation ARTs (VA-ARTs) for Automated Parameter Adaptation3.3.1 Activation Maximization Rule; 3.3.2 Confliction Minimization Rule; 3.3.3 Hybrid Integration of AMR and CMR; 3.3.4 Time Complexity Analysis; 3.3.5 Experiments; 3.4 User Preference Incorporation in Fuzzy ART; 3.4.1 General Architecture; 3.4.2 Geometric Interpretation; 3.5 Probabilistic ART for Short Text Clustering; 3.5.1 Procedures of Probabilistic ART; 3.5.2 Probabilistic Learning for Prototype Modeling; 3.6 Generalized Heterogeneous Fusion ART (GHF-ART) for Heterogeneous Data Co-Clustering
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783030029852
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1099674890
  • (OCoLC)1099674890
Label
Adaptive resonance theory in social media data clustering : roles, methodologies, and applications, Lei Meng, Ah-Hwee Tan and Donald C. Wunsch II
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; Preface; Scope; Content; Audience; Acknowledgments; Contents; Theories; 1 Introduction; 1.1 Clustering in the Era of Web 2.0; 1.2 Research Issues and Challenges; 1.2.1 Representation of Social Media Data; 1.2.2 Scalability for Big Data; 1.2.3 Robustness to Noisy Features; 1.2.4 Heterogeneous Information Fusion; 1.2.5 Sensitivity to Input Parameters; 1.2.6 Online Learning Capability; 1.2.7 Incorporation of User Preferences; 1.3 Approach and Methodology; 1.4 Outline of the Book; References; 2 Clustering and Its Extensions in the Social Media Domain; 2.1 Clustering
  • 2.1.1 K-Means Clustering2.1.2 Hierarchical Clustering; 2.1.3 Graph Theoretic Clustering; 2.1.4 Latent Semantic Analysis; 2.1.5 Non-Negative Matrix Factorization; 2.1.6 Probabilistic Clustering; 2.1.7 Genetic Clustering; 2.1.8 Density-Based Clustering; 2.1.9 Affinity Propagation; 2.1.10 Clustering by Finding Density Peaks; 2.1.11 Adaptive Resonance Theory; 2.2 Semi-Supervised Clustering; 2.2.1 Group Label Constraint; 2.2.2 Pairwise Label Constraint; 2.3 Heterogeneous Data Co-Clustering; 2.3.1 Graph Theoretic Models; 2.3.2 Non-Negative Matrix Factorization Models
  • 2.3.3 Markov Random Field Model2.3.4 Multi-view Clustering Models; 2.3.5 Aggregation-Based Models; 2.3.6 Fusion Adaptive Resonance Theory; 2.4 Online Clustering; 2.4.1 Incremental Learning Strategies; 2.4.2 Online Learning Strategies; 2.5 Automated Data Cluster Recognition; 2.5.1 Cluster Tendency Analysis; 2.5.2 Posterior Cluster Validation Approach; 2.5.3 Algorithms Without a Pre-defined Number of Clusters; 2.6 Social Media Mining and Related Clustering Techniques; 2.6.1 Web Image Organization; 2.6.2 Multimodal Social Information Fusion; 2.6.3 User Community Detection in Social Networks
  • 2.6.4 User Sentiment Analysis2.6.5 Event Detection in Social Networks; 2.6.6 Community Question Answering; 2.6.7 Social Media Data Indexing and Retrieval; 2.6.8 Multifaceted Recommendation in Social Networks; References; 3 Adaptive Resonance Theory (ART) for Social Media Analytics; 3.1 Fuzzy ART; 3.1.1 Clustering Algorithm of Fuzzy ART; 3.1.2 Algorithm Analysis; 3.2 Geometric Interpretation of Fuzzy ART; 3.2.1 Complement Coding in Fuzzy ART; 3.2.2 Vigilance Region (VR); 3.2.3 Modeling Clustering Dynamics of Fuzzy ART Using VRs; 3.2.4 Discussion
  • 3.3 Vigilance Adaptation ARTs (VA-ARTs) for Automated Parameter Adaptation3.3.1 Activation Maximization Rule; 3.3.2 Confliction Minimization Rule; 3.3.3 Hybrid Integration of AMR and CMR; 3.3.4 Time Complexity Analysis; 3.3.5 Experiments; 3.4 User Preference Incorporation in Fuzzy ART; 3.4.1 General Architecture; 3.4.2 Geometric Interpretation; 3.5 Probabilistic ART for Short Text Clustering; 3.5.1 Procedures of Probabilistic ART; 3.5.2 Probabilistic Learning for Prototype Modeling; 3.6 Generalized Heterogeneous Fusion ART (GHF-ART) for Heterogeneous Data Co-Clustering
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783030029852
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1099674890
  • (OCoLC)1099674890

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