Books in the Chapman & Hall/CRC Data Mining and Knowledge Discovery Series series

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

In this book, top researchers from around the world cover the entire area of clustering, from basic methods to more refined and complex data clustering approaches. They pay special attention to recent issues in graphs, social networks, and other domains. The book explores the characteristics of clustering problems in a variety of application areas. It also explains how to glean detailed insight from the clustering process¿including how to verify the quality of the underlying clusters¿through supervision, human intervention, or the automated generation of alternative clusters.

This textbook provides a tutorial-based introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The book follows the format of the first edition, but with updates and additions throughout.

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.

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.

The book is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data analytics using tools developed in Python, such as SciKit Learn, Pandas, Numpy, etc.

Compatible with SAS version 9, SAS Enterprise Guide, and SAS Learning Edition, this resource describes statistical data mining concepts and methods and includes 13 user-friendly SAS macro applications for performing complete data mining tasks. Each chapter emphasizes step-by-step instructions for using SAS macros and interpreting the results.

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.

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.

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" ΓÇô good, bad, and ugly ΓÇô features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data.The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing.The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor''s solution manual is available.About the Author:Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).

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 book and software tools cover all relevant steps of the data mining process. The software and their extensions can be freely downloaded at www.RapidMiner.com.

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 developments in healthcare computing and discusses emerging technologies that can help improve the health and well-being of patients. Written by prominent researchers and experts working in the healthcare domain, it sheds light on the computational challenges in the field of medical informatics.

Educational data mining (EDM) is an emerging discipline concerned with developing methods for exploring the different types of data that come from an educational context. This book presents the applications of data mining techniques in education.

Includes material on geographic knowledge discovery, geographic data warehouse research, map cubes, spatial dependency, spatial clustering methods, clustering techniques for trajectory data, INGENS 2.0 and geovisualization techniques. This title provides chapters on knowledge discovery from spatiotemporal and mobile objects databases.

Feature selection is an essential step for successful data mining applications and has practical significance in many areas, such as statistics, pattern recognition, machine learning, and knowledge discovery. This book covers the key concepts, representative approaches, and inventive applications of various aspects of feature selection.

Identifying some of the most influential algorithms that are widely used in the data mining community, this book provides a description of each algorithm, discusses the impact of the algorithms, and reviews research on the algorithms.

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.

Presents a fresh approach to knowledge discovery in adversarial settings. Focusing on the four main applications areas in knowledge discovery (prediction, clustering, relationship discovery, and textual analysis), this book discusses opportunities for concealment that exist and recommends tactics that can aid in detecting them.

Focuses on statistical methods for text mining and analysis. This work examines methods to automatically cluster and classify text documents and applies these methods in a variety of areas, including adaptive information filtering, information distillation, and text search.

Covers the capabilities and limitations of constrained clustering. This title presents various types of constraints for clustering, describes useful variations of the standard problem of clustering under constraints, and applies clustering with constraints to relational, bibliographic, and video data.

Presents comprehensive data mining concepts, theories and applications in biological and medical research. This book discusses challenge and opportunities in analyzing and mining biological sequences and structures to gain insight into molecular functions. It describes the relationships between data mining and related areas of computing.

Exploring how to extract knowledge structures from evolving and time-changingdata, "Knowledge Discovery from Data Streams" presents a coherent overview ofstate-of-the-art research in learning from data streams.

Focusing on data mining mechanics and applications, this book explores some of the most common matrix decompositions, including singular value, semidiscrete, independent component analysis, non-negative matrix factorization, and tensors. It also discusses several important theoretical and algorithmic problems of matrix decompositions.

Assuming no prior knowledge of mathematics or data mining, this self-contained book presents a "do-it-yourself" approach to extracting interesting patterns from graph data. Each chapter focuses on a graph mining task, such as link analysis, cluster analysis, and classification. Through many applications, the book demonstrates how computational techniques can help solve real-world problems. Every algorithm and example is accompanied with R code, allowing readers to see how the algorithmic techniques correspond to the process of graph data analysis and to use the graph mining techniques in practice.

Covers privacy and anonymity for data mining applications. This book presents novel application domains, such as data mining of biomedical and healthcare data. It addresses spatial, temporal, and spatio-temporal data as well graphs, links, and social networks. It details privacy-aware data publishing and mining of data streams.