Modern Technologies for Big Data Classification and Clustering 1st Edition by Hari Seetha, M Narasimha Murty, BK Tripathy ISBN 1522528075 9781522528074

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ISBN 10: 1522528075 
ISBN 13: 9781522528074
Author: Hari Seetha, M Narasimha Murty, BK Tripathy

Data has increased due to the growing use of web applications and communication devices. It is necessary to develop new techniques of managing data in order to ensure adequate usage. Modern Technologies for Big Data Classification and Clustering is an essential reference source for the latest scholarly research on handling large data sets with conventional data mining and provide information about the new technologies developed for the management of large data. Featuring coverage on a broad range of topics such as text and web data analytics, risk analysis, and opinion mining, this publication is ideally designed for professionals, researchers, and students seeking current research on various concepts of big data analytics.

Modern Technologies for Big Data Classification and Clustering 1st Table of contents:

Chapter 1: Uncertainty-Based Clustering Algorithms for Large Data Sets
ABSTRACT
1. INTRODUCTION
2. TYPES OF CLUSTERING ALGORITHMS FOR LARGE DATA
3. CHARACTERISTICS OF BIG DATA
4. CHARACTERISTICS OF A GOOD LARGE DATA CLUSTERING ALGORITHM (FAHAD ET AL., 2014)
5. DIFFERENT BIG DATA CLUSTERING ALGORITHMS
6. SCOPE FOR FUTURE STUDY
7. CONCLUSION
REFERENCES
KEY TERMS AND DEFINITIONS
Chapter 2: Sentiment Mining Approaches for Big Data Classification and Clustering
ABSTRACT
INTRODUCTION
PROBLEM OF SENTIMENT ANALYSIS ON BIG DATA
MACHINE-LEARNING-BASED APPROACH
LEXICON-BASED APPROACH
ENSEMBLE LEARNING METHODS
HYBRID APPROACH
EXTENSIONS OF CLASSIFICATION AND CLUSTERING
APPLICATIONS
SOFTWARE TOOLS
CONCLUSION
REFERENCES
Chapter 3: Data Compaction Techniques
ABSTRACT
INTRODUCTION
FURTHER READINGS
REFERENCES
KEY TERMS AND DEFINITIONS
Chapter 4: Methodologies and Technologies to Retrieve Information From Text Sources
ABSTRACT
INTRODUCTION
BACKGROUND
TECHNOLOGIES USED IN THE EXTRACTION OF TEXT INFORMATION
METHODOLOGIES: TO ANALYZE THE EXTRACTION INFORMATION
CONCLUSION
REFERENCES
Chapter 5: Twitter Data Analysis
ABSTRACT
INTRODUCTION TO TWITTER DATA ANALYSIS
RESEARCH BACKGROUND
TWITTER DATA EXTRACTION
TRENDING TOPIC ANALYSIS
INFORMATION DIFFUSION: A BIG DATA APPROACH
DISCUSSIONS
REFERENCES
KEY TERMS AND DEFINITIONS
Chapter 6: Use of Social Network Analysis in Telecommunication Domain
ABSTRACT
INTRODUCTION
ANALYSIS OF SOCIAL NETWORK IN TELECOM SECTOR
SOCIAL NETWORK ANALYSIS IN CHURN PREDICTION OF TELECOM DATA
SOCIAL NETWORK ANALYSIS IN SMS PATTERN BEHAVIOR OF CUSTOMERS
EMPIRICAL ANALYSIS OF SMS GRAPHS
CONCLUSION
REFERENCES
Chapter 7: A Review on Spatial Big Data Analytics and Visualization
ABSTRACT
INTRODUCTION
DATA COLLECTION
GEO-SPATIAL DATA AS BIG DATA
HADOOP FRAMEWORK FOR SPATIAL DATA ANALYSIS
APACHE MAHOUT
VISUALIZATION
BIGDATA VISUALIZATION METHODS
CONCLUSION
REFERENCES
KEY TERMS AND DEFINITIONS
APPENDIX
Chapter 8: A Survey on Overlapping Communities in Large-Scale Social Networks
ABSTRACT
1. INTRODUCTION
2. PRELIMINARIES
3. OVERLAPPING COMMUNITY DETECTION ALGORITHMS
4. EVALUATION CRITERIA FOR OVERLAPPING COMMUNITY DETECTION ALGORITHMS
5. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
REFERENCES
KEY TERMS AND DEFINITIONS
Chapter 9: A Brief Study of Approaches to Text Feature Selection
ABSTRACT
1. INTRODUCTION
2. APPROACHES TO FEATURE SELECTION
3. SPECIFIC ISSUES WITH PRACTICAL TEXT DATA PROCESS AND FEATURE REDUCTION
4. EXPERIMENTS ON FEATURE SELECTION
5. CONCLUSION
6. FURTHER RESEARCH DIRECTIONS
REFERENCES
APPENDIX
Chapter 10: Biological Big Data Analysis and Visualization
ABSTRACT
INTRODUCTION
BIOINFORMATICS – BIG DATA
NGS DATA ANALYSIS, INTERPRETATION AND VISUALIZATION
CONCLUSION
REFERENCES
KEY TERMS AND DEFINITIONS
APPENDIX 1
APPENDIX 2

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