Advanced Data Mining Tools and Methods for Social Computing explores advances in the latest data mining tools, methods, algorithms and the architectures being developed specifically for social computing and social network analysis. The book reviews major emerging trends in technology that are supporting current advancements in social networks, including data mining techniques and tools. It also aims to highlight the advancement of conventional approaches in the field of social networking. Chapter coverage includes reviews of novel techniques and state-of-the-art advances in the area of data mining, machine learning, soft computing techniques, and their applications in the field of social network analysis. - Provides insights into the latest research trends in social network analysis - Covers a broad range of data mining tools and methods for social computing and analysis - Includes practical examples and case studies across a range of tools and methods - Features coding examples and supplementary data sets in every chapter

This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining.

This book, bringing together selected papers from the 10th International Conference on Entrepreneurship, Business and Technology (InCEBT) on the overarching theme of ‘Industry Forward and Technology Transformation in Business and Entrepreneurship’, provides the audience some preliminary understanding of the current and emerging trends in entrepreneurship and business activities. This includes the usage of information and digital technology in business, competition in a digital economy, its challenges and opportunities, and transformation of business and entrepreneurship for the forward industry.

First published Open Access under a Creative Commons license as What is Online Research?, this title is now also available as part of the Bloomsbury Research Methods series. This book provides a concise and accessible introduction to online research, covering ethics, surveys, focus groups, ethnographies, experiments and the gathering and analysis of naturally occurring digital/big data. It also asks how researchers should use the digital environment to communicate their research and looks forward to the future of the field, asking what the next ten years hold. Online research is rarely well served by the direct translation of onsite methods onto the internet. Rather, researchers need to reflect, adapt and redesign research as they change the mode through which they conduct their research. Featuring an updated glossary, two new chapters and comprehensive updates throughout, this new edition provides new and experienced researchers with the foundation they need to conduct online research projects.

Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. - Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects - Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields - Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data