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"This book provides an analysis of large data in the field of classification and clustering by presenting algorithms and comparative analysis in the form of their effectiveness and efficiency. It covers topics such as handling large data with conventional data mining, machine learning algorithms and information about new technologies, algorithms and platforms developed for handling large data"--.

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.

• Title Page
• Copyright Page
• Book Series
• Table of Contents
• Detailed Table of Contents
• Preface
• Chapter 1: Uncertainty-Based Clustering Algorithms for Large Data Sets
• Chapter 2: Sentiment Mining Approaches for Big Data Classification and Clustering
• Chapter 3: Data Compaction Techniques
• Chapter 4: Methodologies and Technologies to Retrieve Information From Text Sources
• Chapter 5: Twitter Data Analysis
• Chapter 6: Use of Social Network Analysis in Telecommunication Domain
• Chapter 7: A Review on Spatial Big Data Analytics and Visualization
• Chapter 8: A Survey on Overlapping Communities in Large-Scale Social Networks
• Chapter 9: A Brief Study of Approaches to Text Feature Selection
• Chapter 10: Biological Big Data Analysis and Visualization
• Related References
• Compilation of References
• About the Contributors
• Index