Explore the vast range of titles available.

Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data- including stream data, sequence data, graph structured data, social network data, and multi-relational data.

A comprehensive compilation of new developments in data linkage methodology The increasing availability of large administrative databases has led to a dramatic rise in the use of data linkage, yet the standard texts on linkage are still those which describe the seminal work from the 1950-60s, with some updates. Linkage and analysis of data across sources remains problematic due to lack of discriminatory and accurate identifiers, missing data and regulatory issues. Recent developments in data linkage methodology have concentrated on bias and analysis of linked data, novel approaches to organising relationships between databases and privacy-preserving linkage. Methodological Developments in Data Linkage brings together a collection of contributions from members of the international data linkage community, covering cutting edge methodology in this field. It presents opportunities and challenges provided by linkage of large and often complex datasets, including analysis problems, legal and security aspects, models for data access and the development of novel research areas.  New methods for handling uncertainty in analysis of linked data, solutions for anonymised linkage and alternative models for data collection are also discussed. Key Features: * Presents cutting edge methods for a topic of increasing importance to a wide range of research areas, with applications to data linkage systems internationally * Covers the essential issues associated with data linkage today * Includes examples based on real data linkage systems, highlighting the opportunities, successes and challenges that the increasing availability of linkage data provides * Novel approach incorporates technical aspects of both linkage, management and analysis of linked data This book will be of core interest to academics, government employees, data holders, data managers, analysts and statisticians who use administrative data. It will also appeal to researchers in a variety of areas, including epidemiology, biostatistics, social statistics, informatics, policy and public health.

Examine the problem of maintaining the quality of big data and discover novel solutions. You will learn the four V's of big data, including veracity, and study the problem from various angles. The solutions discussed are drawn from diverse areas of engineering and math, including machine learning, statistics, formal methods, and the Blockchain technology. Veracity of Big Data serves as an introduction to machine learning algorithms and diverse techniques such as the Kalman filter, SPRT, CUSUM, fuzzy logic, and Blockchain, showing how they can be used to solve problems in the veracity domain. Using examples, the math behind the techniques is explained in easy-to-understand language. Determining the truth of big data in real-world applications involves using various tools to analyze the available information. This book delves into some of the techniques that can be used. Microblogging websites such as Twitter have played a major role in public life, including during presidential elections. The book uses examples of microblogs posted on a particular topic to demonstrate how veracity can be examined and established. Some of the techniques are described in the context of detecting veiled attacks on microblogging websites to influence public opinion. -- Back cover.

Machine Learning for Healthcare: Handling and Managing Data will provide in-depth information about handling and managing healthcare data through machine learning methods. This book will express the long-standing challenges in healthcare informatics and provide rational explanations of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. This book may appeal to Computer Science/Information Technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector. The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors.

This book presents techniques for querying and managing Linked Data that is available on today's Web. It shows how the abundance of Linked Data can serve as fertile ground for research and commercial applications. While the book covers query processing extensively, the Linked Data abstraction furnishes more than a mechanism for collecting, integrating, and querying data from the open Web-the Linked Data technology stack also allows for controlled, sophisticated applications deployed in an enterprise environment.

Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning   This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates.

The aim of this book is to provide an internationally respected collection of scientific research methods, technologies and applications in the area of data science. This book can prove useful to the researchers, professors, research students and practitioners as it reports novel research work on challenging topics in the area surrounding data science. In this book, some of the chapters are written in tutorial style concerning machine learning algorithms, data analysis, information design, infographics, relevant applications, etc. The book is structured as follows: • Part I: Data Science: Theory, Concepts, and Algorithms This part comprises five chapters on data Science theory, concepts, techniques and algorithms. • Part II: Data Design and Analysis This part comprises five chapters on data design and analysis. • Part III: Applications and New Trends in Data Science This part comprises four chapters on applications and new trends in data science. Preface Editors Contributors Part I Data Science: Theory, Concepts, and Algorithms Framework for Visualization of GeoSpatial Query Processing by Integrating MongoDB with Spark S.Vasavi, P.Vamsi Krishna, Anu A. Gokhale A Study on Meta-heuristic based Neural Networks for Image Segmentation Purposes Navid Razmjooy, Vania V. Estrela, Hermes J. Loschi Study and Analysis of a Feature Subset Selection Technique using Penguin Search Optimization Algorithm Agnip Dasgupta, Ardhendu Banerjee, Aniket Ghosh Dastidar, Antara Barman, Sanjay Chakraborty A Physical Design Strategy on a NoSQL DBMS Marcos Jota, Marlene Goncalves, Ritces Parra Large-Scale Distributed Stream Data Collection Schemes Tomoya Kawakami, Tomoki Yoshihisa, and Yuuichi Teranishic Part II Data Design and Analysis Big Data Analysis and Management in Healthcare R.DHAYA, R.KANTHAVEL, FAHAD ALGARNI, M.DEVI Healthcare Analytics: A Case Study Approach using the Framingham Heart Study Carol Hargreaves Bioinformatics Analysis of Dysfunctional (mutated) Proteins of Cardiac Ion Channels Underlying the Brugada Syndrome Carlos Polanco, Manlio F. Márquez, Vladimir N. Uversky, Thomas Buhse, and Miguel Arias Estrada Discrimination of Healthy Skin, Superficial Epidermal Burns and Full-thickness Burns from 2D-Coloured Images Using Machine Learning Aliyu Abubakar, Hassan Ugail, Ali Maina Bukar, Kirsty M. Smith A Study and Analysis of an Emotion Classification and State Transition System in Brain Computer Interfacing Subhadip Pal, Shailesh Shaw, Tarun Saurabh, Yashwant Kumar, Sanjay Chakraborty Part III Applications and New Trends in Data Science Comparison of Gradient and Textural Features for Writer Retrieval in Handwritten Documents Mohamed Lamine Bouibed, Hassiba Nemmour and Youcef Chibani A Supervised Guest Satisfaction Classification with Reviews Text and Ratings Himanshu Sharma, Aakash and Anu G. Aggarwal Sentiment Analysis for Decision Making Using Machine Learning Algorithms Mohamed Alloghani, Thar Baker, Abir Hussain, Mohammed Khalaf, Jamila MUSTAFINA, Mohammed Al-Khafajiy Deep Learning Model: Emotion Recognition from Continuous Action Video R. Santhosh kumar, M. Kalaiselvi Geetha Index Qurban A Memon has contributed at levels of teaching, research, and community service in the area of electrical and computer engineering. He has authored/co‐authored ninety seven publications over a period of about eighteen years of his academic career. Shakeel Khoja is a Professor at Faculty of Computer Science, IBA and a Commonwealth Academic Fellow. He carries an overall professional experience of over 18 years and has over fifty research publications to his credit. He is also a reviewer of number of journals and conferences in the field of computer science.

This book introduces the semantic aspects of natural language processing and its applications. Topics covered include: measuring word meaning similarity, multi-lingual querying, and parametric theory, named entity recognition, semantics, query language, and the nature of language. The book also emphasizes the portions of mathematics needed to under