Bøger i Chapman & Hall/CRC Data Mining and Knowledge Discovery Series serien

"A CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa plc."

Supplying a comprehensive overview of healthcare analytics research, Healthcare Data Analytics provides an understanding of the analytical techniques currently available to solve healthcare problems. The book details novel techniques for acquiring, handling, retrieving, and making best use of healthcare data. It analyzes recent devel

This book describes a novel approach to scientific research that uses unstructured data analysis as a generative tool for new hypotheses. The author develops a systematic process for leveraging heterogeneous structured and unstructured data sources, data mining, and computational architectures to make the discovery process faster and more effect

With a focus on computing system management, this book presents a variety of event mining approaches for improving the quality and efficiency of IT service and system management. It covers different components in the data-driven framework, from system monitoring and event generation to pattern discovery and summarization. The book explores recen

This book provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python. The contributors explain how text data are gathered and processed from a wide variety of sources, including books, server access logs, websites, social media sites, and message boards. Each chap

This book provides a comprehensive introduction to the use of graph analysis in the study of social media and digital media. It covers the following topics: graphs in social media, graph theory, algebraic analysis of graphs, graph clustering, diffusion in social media, label propagation in graphs, graphs in pattern recognition and machine learni

If you want to learn how to analyze your data with R, this is your book. A broad range of real-world case studies highlights the breadth and depth of the R software. This expanded second edition delves deeper into topical explanations and updates and expands all case studies. Assuming no prior knowledge of R or data mining/statistical techniques

Large-scale machine learning could prove highly beneficial in the study of earth science, a broad, multidisciplinary field of study that generates huge amounts of data. This book is the first to tackle the subject, covering significant issues in earth science and large-scale machine learning techniques with well-known authorities in the field.

This book is designed both as a text for graduate level courses in human resource systems, human capital analytics, and data mining as well as a resource for human capital professionals in all areas of human capital management. New Gender Based Pay Equity Research through the lens of Comparable Worth and Occupational Mobility are included.

This new edition includes some key topics relating to the latest version of MS Office, including use of the ribbon, current Excel file types, Dashboard, and basic Sharepoint integration. It shows how to automate operations, such as curve fitting, sorting, filtering, and analyzing data from a variety of sources.

This class-tested textbook is designed for a semester-long graduate, or senior undergraduate course on Computational Health Informatics. Integrating a computer science perspective with a clinical perspective, the book is designed to prepare computer science students for careers in computational health informatics and medical data analysis.

Covering research at the frontier of this field, this volume presents state-of-the-art privacy-preserving data mining techniques for application domains, such as medicine and social networks, which face the increasing heterogeneity and complexity of new forms of data. Renowned authorities from prominent organizations cover well-established results as well as explore complex domains where privacy issues are generally clear and well defined, but the solutions are still preliminary and in continuous development. The book focuses on intricate, real-world applications in medicine, biology, the web, social networks, and mobility observation systems.

This book shows how machine learning can be applied to address real-world problems in the fourth industrial revolution and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolution and its current impact on organizations and society.

This class-tested textbook is designed for a semester-long graduate, or senior undergraduate course on Computational Health Informatics. Integrating a computer science perspective with a clinical perspective, the book is designed to prepare computer science students for careers in computational health informatics and medical data analysis.

Large-scale machine learning could prove highly beneficial in the study of earth science, a broad, multidisciplinary field of study that generates huge amounts of data. This book is the first to tackle the subject, covering significant issues in earth science and large-scale machine learning techniques with well-known authorities in the field.

This book describes a novel approach to scientific research that uses unstructured data analysis as a generative tool for new hypotheses. The author develops a systematic process for leveraging heterogeneous structured and unstructured data sources, data mining, and computational architectures to make the discovery process faster and more effective. This process accelerates human creativity by allowing scientists and inventors to more readily analyze and comprehend the space of possibilities, compare alternatives, and discover entirely new approaches.

This practical guide illustrates the use of state-of-the-art machine learning and data mining techniques in astronomy. The book presents issues in the astronomical sciences that are also important to health, social, and physical sciences. It describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In addition, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications.

Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. This volume presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications, it illustrates important data analysis methods, including mathematical optimization, machine learning, signal processing, and temporal and spatial analysis, for quantifying and describing sustainable development problems.

Written by leaders in the data mining community, including the developers of the RapidMiner software, this book provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors. It presents the most powerful and flexible open source software solutions: RapidMiner and RapidAnalytics. The new edition will update all chapters to RapidMiner 7, and will add at least six new chapters, including new chapters on text mining, time series, and educational data mining.

If you want to learn how to analyze your data with R, this is your book. A broad range of real-world case studies highlights the breadth and depth of the R software. This expanded second edition delves deeper into topical explanations and updates and expands all case studies. Assuming no prior knowledge of R or data mining/statistical techniques, the book covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools.

This volume presents comprehensive data mining concepts, theories, and applications in current biological and medical research. The first section of the book discusses challenges and opportunities in analyzing and mining biological sequences and structures to gain insight into molecular functions. The second section addresses emerging computational challenges in interpreting high-throughput omics data. The book then describes the relationships between data mining and related areas of computing, including knowledge representation, information retrieval, and data integration for structured and unstructured biological data. The last part explores data mining opportunities for biomedical applications.

Exploring existing and emerging work in the field, this volume shows how specification mining techniques can help find software bugs and improve program understanding. Top researchers in the software engineering community provide valuable insight on up-to-date case studies of various software systems, including open source programs and those used by Microsoft Research and IBM Research. The book focuses on mining both finite state machines and temporal rules/patterns of behavior. It presents approaches that use static analysis, dynamic analysis, and combinations of the two.

Through the techniques of data mining, this book demonstrates how to effectively design business processes and develop competitive products and services. It discusses how data mining can identify valuable consumer patterns, which aid marketers and designers in detecting consumers¿ needs. The book also explores visualization tools based on the computational methods of data mining. Discourse analysis, chance discovery, knowledge discovery, formal concept analysis, and an adjacency matrix are just some of the novel approaches covered. The book explains how these methods can be applied to website design, personalized e-commerce support tools, and more.