Modern Technologies for Big Data Classification and Clustering

Modern Technologies for Big Data Classification and Clustering

Hari Seetha, M. Narasimha Murty, B. K. Tripathy

Copyright: © 2018 |Pages: 360

ISBN13: 9781522528050|ISBN10: 1522528059|EISBN13: 9781522528067

DOI: 10.4018/978-1-5225-2805-0

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MLA

Seetha, Hari, et al., editors. Modern Technologies for Big Data Classification and Clustering. IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2805-0

APA

Seetha, H., Murty, M. N., & Tripathy, B. K. (Eds.). (2018). Modern Technologies for Big Data Classification and Clustering. IGI Global. https://doi.org/10.4018/978-1-5225-2805-0

Chicago

Seetha, Hari, M. Narasimha Murty, and B. K. Tripathy, eds. Modern Technologies for Big Data Classification and Clustering. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2805-0

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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.

Table of Contents

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Front Materials

Title Page

Copyright Page

Advances in Data Mining and Database Management (ADMDM) Book Series

Preface

Chapters

Chapter 1

Uncertainty-Based Clustering Algorithms for Large Data Sets (pages 1-33)

B. K. Tripathy, Hari Seetha, M. N. Murty

Data clustering plays a very important role in Data mining, machine learning and Image processing areas. As modern day databases have inherent uncertainties, many uncertainty-based data clustering algorithms have been developed in...

Chapter 2

Sentiment Mining Approaches for Big Data Classification and Clustering (pages 34-63)

Ashok Kumar J, Abirami S, Tina Esther Trueman

Sentiment analysis is one of the most important applications in the field of text mining. It computes people's opinions, comments, posts, reviews, evaluations, and emotions which are expressed on products, sales, services...

Chapter 3

Data Compaction Techniques (pages 64-98)

R. Raj Kumar, P. Viswanath, C. Shoba Bindu

A large dataset is not preferable as it increases computational burden on the methods operating over it. Given the Large dataset, it is always interesting that whether one can generate smaller dataset which is a subset or a set...

Chapter 4

Methodologies and Technologies to Retrieve Information From Text Sources (pages 99-123)

Anu Singha, Phub Namgay

A tool which algorithmically traces the effectiveness of the text files would be helpful in determining whether the text file have all the characteristic of important concepts. Every text source is build up on key phrases, and these...

Chapter 5

Twitter Data Analysis (pages 124-151)

Chitrakala S

Analyzing Social network data using Big Data Tools and techniques promises to provide information that could be of use in recommendation systems, personalized service and many other applications. A few of the analytics that do this...

Chapter 6

Use of Social Network Analysis in Telecommunication Domain (pages 152-178)

Sushruta Mishra, Brojo Kishore Mishra, Hrudaya Kumar Tripathy, Monalisa Mishra, Bijayalaxmi Panda

Social network analysis (SNA) is the analysis of social communication through network and graph theory. In our chapter the application of SNA has been explored in telecommunication domain. Telecom data consist of Customer data and...

Chapter 7

A Review on Spatial Big Data Analytics and Visualization (pages 179-197)

Bangaru Kamatchi Seethapathy, Parvathi R

Spatial dataset, which is becoming nontraditional due to the increase in usage of social media sensor networks, gaming and many other new emerging technologies and applications. The wide variety of sensors are used in solving real...

Chapter 8

A Survey on Overlapping Communities in Large-Scale Social Networks (pages 198-215)

S Rao Chintalapudi, H. M. Krishna Prasad M

Social network analysis is one of the emerging research areas in the modern world. Social networks can be adapted to all the sectors by using graph theory concepts such as transportation networks, collaboration networks, and...

Chapter 9

A Brief Study of Approaches to Text Feature Selection (pages 216-243)

Ravindra Babu Tallamaraju, Manas Kirti

With reducing cost of storage devices, increasing amounts of data is being stored and processed for extracting intelligence. Classification and clustering have been two major approaches in generating data abstraction. Over the last...

Chapter 10

Biological Big Data Analysis and Visualization: A Survey (pages 244-259)

Vignesh U, Parvathi R

The chapter deals with the big data in biology. The largest collection of biological data maintenance paves the way for big data analytics and big data mining due to its inefficiency in finding noisy and voluminous data from normal...

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