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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.

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 presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains. The author first covers core descriptive and inferential statistics for analytics and then enhances numerical statistical techniques with symbolic artificial intelligence and machine learning techniques for richer predictive and prescriptive analytics. Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies.

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.

This book provides a comprehensive overview of various data mining tools and techniques that are increasingly being used by researchers in the international astronomy community. It explores this new problem domain, discussing how it could lead to the development of entirely new algorithms. Leading contributors introduce data mining methods and then describe how the methods can be implemented into astronomy applications. The last section of the book discusses the Redshift Prediction Competition, which is an astronomy competition in the style of the Netflix Prize.

Advances in digital music technology have created challenges for effectively accessing and interacting with large collections of music and associated data. This book explores how data mining addresses these challenges by mining useful information and using it to create novel ways of interacting with large music collections. Leading experts in data mining, machine learning, and music science examine fundamental issues of classification and audio signal processing and discuss social aspects of music mining. They also present new research in instrument recognition, mood and emotion classification, and hit song prediction science.

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.

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.

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.

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.

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.

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.

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.

Defines multimedia data mining, its theory and its applications. This book discusses the theoretical foundations of multimedia data mining, presenting commonly used feature representation, knowledge representation and soft computing techniques. It provides application examples that showcase the potential of multimedia data mining technologies.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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 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.

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.