Granular Computing and Big Data Advancements 1st Edition by Chao Zhang, Wentao Li ISBN 9798369342923 by Igi Global instant download after payment.

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ISBN 13: 9798369342923
Author: Chao Zhang, Wentao Li

In an era defined by the deluge of data, navigating the complexities of decision-making under conditions of uncertainty has emerged as a formidable challenge for scholars and practitioners alike. The sheer volume and velocity of information inundating decision-makers often leads to paralysis or misguided choices, amplifying the risks inherent in uncertain environments. Granular Computing and Big Data Advancements provides insights and solutions in this challenging landscape. This comprehensive publication addresses the fundamental dilemma decision-makers face: how to harness the power of big data amidst uncertainty to make informed choices that drive success. By delving into the intricacies of big data analytics, uncertainty modeling, and advanced quantification techniques, the book equips scholars with the necessary tools to extract meaningful insights from vast datasets and confidently navigate uncertainty. Through real-world case studies spanning diverse domains such as finance, healthcare, and marketing, readers gain invaluable practical insights into applying these methodologies in their contexts. The impact of Granular Computing and Big Data Advancements reverberates across the research community, offering a cohesive resource that bridges the gap between theory and practice. With its interdisciplinary approach and emphasis on innovation, the book fosters collaboration and empowers scholars to tackle complex challenges head-on. Whether researchers seek novel methodologies, practitioners aim to enhance decision-making processes, or students embark on their academic journey, this publication serves as a cornerstone in the quest for effective decision-making amidst the uncertainties of the modern world.

Granular Computing and Big Data Advancements 1st Table of contents:

Chapter 1: Generalized Multi-Granulation Double-Quantitative Decision-Theoretic Rough Set of Multi-Source Information System
ABSTRACT
1. INTRODUCTION
2. PRELIMINARIES
3. MS-GMDQ-DTRS: GENERALIZED MULTI-GRANULATION DOUBLE-QUANTITATIVE DECISION-THEORETIC ROUGH SET MODEL OF MULTI-SOURCE INFORMATION SYSTEM
4. CASE STUDY
5. EXPERIMENTAL ANALYSIS
6. CONCLUSION
ACKNOWLEDGMENT
REFERENCES
Chapter 2: Multi-Source Information Fusion Models and Methods Based on Granular Computing
ABSTRACT
INTRODUCTION
BASIC CONCEPTS AND DEFINITIONS
MULTI-SOURCE HOMOGENEOUS INFORMATION FUSION MODEL
HETEROGENEOUS ATTRIBUTE FUSION METHODS
CONCLUSION
REFERENCES
Chapter 3: Community Discovery in Complex Network Big Data
ABSTRACT
INTRODUCTION
THEORETICAL BASIS OF COMMUNITY DISCOVERY
METHODS FOR COMMUNITY DISCOVERY
COMMUNITY DISCOVERY IN BIG DATA
COMMUNITY DISCOVERY IN TOPOLOGICALLY INCOMPLETE NETWORKS (TIN)
SUBGRAPH EXPANSION METHODS IN LOCAL COMMUNITY DETECTION
COMMUNITY DISCOVERY BASED ON DEEP LEARNING
SUMMARY
REFERENCES
Chapter 4: Information Granule, Modeling, and Application From the Perspective of Granular Computing
ABSTRACT
INTRODUCTION
BASIC KNOWLEDGE
MAIN RESULTS
MODELING
APPLICATIONS
CONCLUSION
ACKNOWLEDGMENT
REFERENCES
Chapter 5: A Novel Sequential Three-Way Decision With Rough Fuzzy Sets Based on Optimal Granularity Selection
ABSTRACT
1. INTRODUCTION
2. PRELIMINARIES
3. SEQUENTIAL THREE-WAY DECISIONS WITH ROUGH FUZZY SETS
4. COST OF MULTILEVEL DECISION RESULT IN S3WDRFS MODEL
5. OPTIMAL QUALITATIVE COST-SENSITIVE GRANULARITY SELECTION
6. EXPERIMENTS AND ANALYSIS
7. CONCLUSION
REFERENCES
Chapter 6: Multi-Granulation-Based Optimal Scale Selection in Multi-Scale Information Systems
ABSTRACT
1. INTRODUCTION
2. RELATED FUNDAMENTAL WORKS
3. OPTIMISTIC MULTIGRANULATION OPTIMAL SCALE SELECTION FOR MULTI-SCALE DECISION TABLES
4. CONCLUSION
REFERENCES
Chapter 7: Fuzzy Hamacher Aggregation Functions and Their Applications to Multiple Attribute Decision Making
ABSTRACT
1. INTRODUCTION
2. PRELIMINARIES
3. PROPOSITIONS ABOUT HAMACHER AGGREGATION OPERATORS IN MADM
4. THEORETICAL PROOF OF TWO PROPOSITIONS
5. A METHOD FOR RANKING ALTERNATIVES IN MADM PROBLEMS WITH DHF ASSESSMENTS UNDER THE TWO PROPOSITIONS
6. CONCLUSION
ACKNOWLEDGMENT
REFERENCES
Chapter 8: The Researches on Knowledge Distance and Its Relative Extensions
ABSTRACT
INTRODUCTION
KNOWLEDGE DISTANCE
RELATIVE KNOWLEDGE DISTANCE
CASE ANALYSIS
CONCLUSION
DATA AVAILABILITY STATEMENT
ACKNOWLEDGMENT
REFERENCES
Chapter 9: Feasibility Evaluation of Highwall Mining in Open-Pit Coal Mine Based on Variable Weight Fuzzy Theory
ABSTRACT
INTRODUCTION
PRELIMINARIES
FEASIBILITY EVALUATION MODEL
CASE ANALYSIS
CONCLUSION
DATA AVAILABILITY STATEMENT
ACKNOWLEDGMENT
REFERENCES
Chapter 10: Trusted Fine-Grained Image Classification Based on Evidence Theory and Its Applications to Medical Image Analysis
ABSTRACT
INTRODUCTION
TRUSTED FGIC BASED ON EVIDENCE THEORY
EXPERIMENTS
CONCLUSION
REFERENCES
Compilation of References
About the Contributors
Index

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