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E-raamat: Adaptive Resonance Theory in Social Media Data Clustering: Roles, Methodologies, and Applications

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Adaptive Resonance Theory in Social Media Data Clustering: Roles, Methodologies, and ApplicationsSuurem pilt
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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 analytics

Clustering as a fundamental technique for unsupervised knowledge discovery and data mining

A 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 domain







Adaptive 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 ARTs 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 users interests by learning from online posts and behaviors?

How to create a personalized search engine by automatically indexing and searching multimodal information resources?          











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Part I Theories
1 Introduction
3(12)
1.1 Clustering in the Era of Web 2.0
3(2)
1.2 Research Issues and Challenges
5(5)
1.2.1 Representation of Social Media Data
5(2)
1.2.2 Scalability for Big Data
7(1)
1.2.3 Robustness to Noisy Features
7(1)
1.2.4 Heterogeneous Information Fusion
8(1)
1.2.5 Sensitivity to Input Parameters
8(1)
1.2.6 Online Learning Capability
9(1)
1.2.7 Incorporation of User Preferences
9(1)
1.3 Approach and Methodology
10(3)
1.4 Outline of the Book
13(2)
References
13(2)
2 Clustering and Its Extensions in the Social Media Domain
15(30)
2.1 Clustering
15(8)
2.1.1 K-Means Clustering
15(1)
2.1.2 Hierarchical Clustering
16(1)
2.1.3 Graph Theoretic Clustering
17(1)
2.1.4 Latent Semantic Analysis
18(1)
2.1.5 Non-Negative Matrix Factorization
18(1)
2.1.6 Probabilistic Clustering
19(1)
2.1.7 Genetic Clustering
19(1)
2.1.8 Density-Based Clustering
20(1)
2.1.9 Affinity Propagation
21(1)
2.1.10 Clustering by Finding Density Peaks
22(1)
2.1.11 Adaptive Resonance Theory
22(1)
2.2 Semi-Supervised Clustering
23(1)
2.2.1 Group Label Constraint
23(1)
2.2.2 Pairwise Label Constraint
24(1)
2.3 Heterogeneous Data Co-Clustering
24(4)
2.3.1 Graph Theoretic Models
24(2)
2.3.2 Non-Negative Matrix Factorization Models
26(1)
2.3.3 Markov Random Field Model
26(1)
2.3.4 Multi-view Clustering Models
27(1)
2.3.5 Aggregation-Based Models
27(1)
2.3.6 Fusion Adaptive Resonance Theory
27(1)
2.4 Online Clustering
28(1)
2.4.1 Incremental Learning Strategies
28(1)
2.4.2 Online Learning Strategies
28(1)
2.5 Automated Data Cluster Recognition
29(2)
2.5.1 Cluster Tendency Analysis
29(1)
2.5.2 Posterior Cluster Validation Approach
30(1)
2.5.3 Algorithms Without a Pre-defined Number of Clusters
30(1)
2.6 Social Media Mining and Related Clustering Techniques
31(14)
2.6.1 Web Image Organization
32(1)
2.6.2 Multimodal Social Information Fusion
33(1)
2.6.3 User Community Detection in Social Networks
33(1)
2.6.4 User Sentiment Analysis
34(1)
2.6.5 Event Detection in Social Networks
34(1)
2.6.6 Community Question Answering
35(1)
2.6.7 Social Media Data Indexing and Retrieval
35(1)
2.6.8 Multifaceted Recommendation in Social Networks
36(1)
References
37(8)
3 Adaptive Resonance Theory (ART) for Social Media Analytics
45(48)
3.1 Fuzzy ART
45(3)
3.1.1 Clustering Algorithm of Fuzzy ART
45(2)
3.1.2 Algorithm Analysis
47(1)
3.2 Geometric Interpretation of Fuzzy ART
48(7)
3.2.1 Complement Coding in Fuzzy ART
48(2)
3.2.2 Vigilance Region (VR)
50(3)
3.2.3 Modeling Clustering Dynamics of Fuzzy ART Using VRs
53(1)
3.2.4 Discussion
54(1)
3.3 Vigilance Adaptation ARTs (VA-ARTs) for Automated Parameter Adaptation
55(16)
3.3.1 Activation Maximization Rule
56(1)
3.3.2 Confliction Minimization Rule
57(1)
3.3.3 Hybrid Integration of AMR and CMR
58(1)
3.3.4 Time Complexity Analysis
59(1)
3.3.5 Experiments
60(11)
3.4 User Preference Incorporation in Fuzzy ART
71(2)
3.4.1 General Architecture
71(1)
3.4.2 Geometric Interpretation
72(1)
3.5 Probabilistic ART for Short Text Clustering
73(3)
3.5.1 Procedures of Probabilistic ART
74(1)
3.5.2 Probabilistic Learning for Prototype Modeling
75(1)
3.6 Generalized Heterogeneous Fusion ART (GHF-ART) for Heterogeneous Data Co-Clustering
76(6)
3.6.1 General Architecture
77(1)
3.6.2 Clustering Procedures
78(1)
3.6.3 Robustness Measure for Feature Modality Weighting
79(2)
3.6.4 Time Complexity Analysis
81(1)
3.7 Online Multimodal Co-indexing ART (OMC-ART) for Streaming Multimedia Data Indexing
82(4)
3.7.1 General Procedures
82(1)
3.7.2 Online Normalization of Features
83(2)
3.7.3 Salient Feature Discovery for Generating Indexing Base of Data
85(1)
3.7.4 Time Complexity Analysis
86(1)
3.8 Discussion
86(7)
References
88(5)
Part II Applications
4 Personalized Web Image Organization
93(18)
4.1 Introduction
93(2)
4.2 Problem Statement and Formulation
95(1)
4.3 Personalized Hierarchical Theme-Based Clustering (PHTC)
95(7)
4.3.1 Overview
95(1)
4.3.2 PF-ART for Clustering Surrounding Text
96(3)
4.3.3 Semantic Hierarchy Generation
99(3)
4.4 Experiments
102(6)
4.4.1 Evaluation Measures
102(1)
4.4.2 NUS-WIDE Dataset
103(3)
4.4.3 Flickr Dataset
106(2)
4.5 Discussion
108(3)
References
109(2)
5 Socially-Enriched Multimedia Data Co-clustering
111(26)
5.1 Introduction
111(2)
5.2 Problem Statement and Formulation
113(1)
5.3 GHF-ART for Multimodal Data Fusion and Analysis
114(6)
5.3.1 Feature Extraction
115(1)
5.3.2 Similarity Measure
116(1)
5.3.3 Learning Strategies for Multimodal Features
117(1)
5.3.4 Self-Adaptive Parameter Tuning
118(1)
5.3.5 Time Complexity Comparison
119(1)
5.4 Experiments
120(13)
5.4.1 NUS-WIDE Dataset
120(8)
5.4.2 Corel Dataset
128(3)
5.4.3 20 Newsgroups Dataset
131(2)
5.5 Discussion
133(4)
References
134(3)
6 Community Discovery in Heterogeneous Social Networks
137(18)
6.1 Introduction
137(2)
6.2 Problem Statement and Formulation
139(1)
6.3 GHF-ART for Clustering Heterogeneous Social Links
139(4)
6.3.1 Heterogeneous Link Representation
139(2)
6.3.2 Heterogeneous Link Fusion for Pattern Similarity Measure
141(1)
6.3.3 Learning from Heterogeneous Links
141(1)
6.3.4 Adaptive Weighting of Heterogeneous Links
142(1)
6.3.5 Computational Complexity Analysis
143(1)
6.4 Experiments
143(9)
6.4.1 YouTube Dataset
144(4)
6.4.2 BlogCatalog Dataset
148(4)
6.5 Discussion
152(3)
References
154(1)
7 Online Multimodal Co-indexing and Retrieval of Social Media Data
155(20)
7.1 Introduction
156(1)
7.2 Problem Statement and Formulation
157(1)
7.3 OMC-ART for Multimodal Data Co-indexing and Retrieval
158(5)
7.3.1 OMC-ART for Online Co-indexing of Multimodal Data
159(3)
7.3.2 Fast Ranking for Multimodal Queries
162(1)
7.3.3 Computational Complexity Analysis
163(1)
7.4 Experiments
163(6)
7.4.1 Data Description
163(1)
7.4.2 Evaluation Measures
164(1)
7.4.3 Parameter Selection
164(1)
7.4.4 Performance Comparison
165(2)
7.4.5 Efficiency Analysis
167(2)
7.5 Real-World Practice: Multimodal E-Commerce Product Search Engine
169(4)
7.5.1 Architecture Overview
169(1)
7.5.2 Prototype System Implementation
170(1)
7.5.3 Analysis with Real-World E-Commerce Product Data
171(2)
7.6 Discussion
173(2)
References
173(2)
8 Concluding Remarks
175(6)
8.1 Summary of Book
175(3)
8.2 Prospective Discussion
178(3)
References
179(2)
Index 181
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