Linking and Mining Heterogeneous and Multi view Data 1st edition by Deepak, Anna Jurek Loughrey ISBN 3030018719 973030018719

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Product details:

ISBN 10:      3030018719
ISBN 13: 978-3030018719
Author:      Deepak P, Anna Jurek-Loughrey

Linking and Mining Heterogeneous and Multi-view Data 1st Table of contents:

 

Chapter 1: Multi-view Data Completion

• Overview and Key Techniques for Data Completion in Multi-view Settings.

Chapter 2: Multi-view Clustering

• Approaches to Clustering in Multi-view Data Scenarios.

Chapter 3: Semi-supervised and Unsupervised Approaches to Record Pairs Classification in Multi-source Data Linkage

• A Comparative Review of Semi-supervised and Unsupervised Classification Methods for Data Linkage.

Chapter 4: A Review of Unsupervised and Semi-Supervised Blocking Methods for Record Linkage

• Exploration of Blocking Techniques for Efficient Record Linkage in Multi-source Datasets.

Chapter 5: Traffic Sensing & Assessing in Digital Transportation Systems

• Techniques and Technologies for Sensing and Analyzing Traffic in Digital Transportation Networks.

Chapter 6: How Did the Discussion Go? Discourse Act Classification in Social Media Conversations

• Methods for Classifying Discourse Acts in Social Media Interactions.

Chapter 7: Entity Linking in Enterprise Search: Combining Textual and Structural Information

• The Integration of Textual and Structural Information for Effective Entity Linking in Enterprise Search.

Chapter 8: Clustering Multi-view Data Using Non-negative Matrix Factorization and Manifold Learning for Effective Understanding: A Survey Paper

• A Survey of Clustering Techniques Combining Non-negative Matrix Factorization and Manifold Learning.

Chapter 9: Leveraging Heterogeneous Data for Fake News Detection

• Approaches for Detecting Fake News by Integrating Heterogeneous Data Sources.

Chapter 10: On the Evaluation of Community Detection Algorithms on Heterogeneous Social Media Data

• Evaluation and Performance of Community Detection Algorithms on Diverse Social Media Data.

Chapter 11: General Framework for Multi-View Metric Learning

• An In-depth Framework for Metric Learning in Multi-view Settings.

Chapter 12: Learning from Imbalanced Datasets with Cross-view Cooperation-based Ensemble Methods

• Techniques for Learning from Imbalanced Datasets through Ensemble Methods Leveraging Cross-view Cooperation.

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