Next Generation Of Data Mining

Drawn from the US National Science Foundation's Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field.Gathering perspectives from top experts across different di

Discover the next generation of data-mining tools and technology This book brings together an international team of eighty experts to present readers with the next generation of data-mining applications. Unlike other publications that take a strictly academic and theoretical approach, this book features authors who have successfully developed data-mining solutions for a variety of customer types. Presenting their state-of-the-art methodologies and techniques, the authors show readers how they can analyze enormous quantities of data and make new discoveries by connecting key pieces of data that may be spread across several different databases and file servers. The latest data-mining techniques that will revolutionize research across a wide variety of fields including business, science, healthcare, and industry are all presented. Organized by application, the twenty-five chapters cover applications in: Industry and business Science and engineering Bioinformatics and biotechnology Medicine and pharmaceuticals Web and text-mining Security New trends in data-mining technology And much more . . . Readers from a variety of disciplines will learn how the next generation of data-mining applications can radically enhance their ability to analyze data and open the doors to new opportunities. Readers will discover: New data-mining tools to automate the evaluation and qualification of sales opportunities The latest tools needed for gene mapping and proteomic data analysis Sophisticated techniques that can be engaged in crime fighting and prevention With its coverage of the most advanced applications, Next Generation of Data-Mining Applications is essential reading for all researchers working in data mining or who are tasked with making sense of an ever-growing quantity of data. The publication also serves as an excellent textbook for upper-level undergraduate and graduate courses in computer science, information management, and statistics.

A state-of-the-art survey of recent advances in data mining or knowledge discovery.

Eight sections of this book span fundamental issues of knowledge discovery, classification and clustering, trend and deviation analysis, dependency derivation, integrated discovery systems, augumented database systems and application case studies. The appendices provide a list of terms used in the literature of the field of data mining and knowledge discovery in databases, and a list of online resources for the KDD researcher.

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining.

Data Mining for Business Applications presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The contributions mark a paradigm shift from “data-centered pattern mining” to “domain driven actionable knowledge discovery” for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in theory and practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future research and development in the dialogue between academia and business.