Author: Pang-Ning Tan Publisher: ISBN: 9780273769224 Category : Data mining Languages : en Pages : 864
Book Description
Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.
Author: Richard J. Roiger Publisher: CRC Press ISBN: 1498763987 Category : Business & Economics Languages : en Pages : 530
Book Description
Provides in-depth coverage of basic and advanced topics in data mining and knowledge discovery Presents the most popular data mining algorithms in an easy to follow format Includes instructional tutorials on applying the various data mining algorithms Provides several interesting datasets ready to be mined Offers in-depth coverage of RapidMiner Studio and Weka’s Explorer interface Teaches the reader (student,) hands-on, about data mining using RapidMiner Studio and Weka Gives instructors a wealth of helpful resources, including all RapidMiner processes used for the tutorials and for solving the end of chapter exercises. Instructors will be able to get off the starting block with minimal effort Extra resources include screenshot sequences for all RapidMiner and Weka tutorials and demonstrations, available for students and instructors alike The latest version of all freely available materials can also be downloaded at: http://krypton.mnsu.edu/~sa7379bt/
Author: R.L. Grossman Publisher: Springer Science & Business Media ISBN: 9781402001147 Category : Computers Languages : en Pages : 632
Book Description
Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.
Author: Usama M. Fayyad Publisher: Morgan Kaufmann ISBN: 9781558606890 Category : Computers Languages : en Pages : 446
Book Description
This text surveys research from the fields of data mining and information visualisation and presents a case for techniques by which information visualisation can be used to uncover real knowledge hidden away in large databases.
Author: Olivia Parr Rud Publisher: John Wiley & Sons ISBN: 0471437514 Category : Computers Languages : en Pages : 399
Book Description
Increase profits and reduce costs by utilizing this collection of models of the most commonly asked data mining questions In order to find new ways to improve customer sales and support, and as well as manage risk, business managers must be able to mine company databases. This book provides a step-by-step guide to creating and implementing models of the most commonly asked data mining questions. Readers will learn how to prepare data to mine, and develop accurate data mining questions. The author, who has over ten years of data mining experience, also provides actual tested models of specific data mining questions for marketing, sales, customer service and retention, and risk management. A CD-ROM, sold separately, provides these models for reader use.
Author: Dorian Pyle Publisher: Elsevier ISBN: 0080500455 Category : Computers Languages : en Pages : 721
Book Description
Business Modeling and Data Mining demonstrates how real world business problems can be formulated so that data mining can answer them. The concepts and techniques presented in this book are the essential building blocks in understanding what models are and how they can be used practically to reveal hidden assumptions and needs, determine problems, discover data, determine costs, and explore the whole domain of the problem. This book articulately explains how to understand both the strategic and tactical aspects of any business problem, identify where the key leverage points are and determine where quantitative techniques of analysis -- such as data mining -- can yield most benefit. It addresses techniques for discovering how to turn colloquial expression and vague descriptions of a business problem first into qualitative models and then into well-defined quantitative models (using data mining) that can then be used to find a solution. The book completes the process by illustrating how these findings from data mining can be turned into strategic or tactical implementations. · Teaches how to discover, construct and refine models that are useful in business situations· Teaches how to design, discover and develop the data necessary for mining · Provides a practical approach to mining data for all business situations· Provides a comprehensive, easy-to-use, fully interactive methodology for building models and mining data· Provides pointers to supplemental online resources, including a downloadable version of the methodology and software tools.
Author: Hillol Kargupta Publisher: AAAI Press ISBN: Category : Computers Languages : en Pages : 504
Book Description
This book presents introductions to DKD and PKD, extensive reviews of the field, and state-of-the-art techniques. Foreword by Vipin Kumar Knowledge discovery and data mining (KDD) deals with the problem of extracting interesting associations, classifiers, clusters, and other patterns from data. The emergence of network-based distributed computing environments has introduced an important new dimension to this problem--distributed sources of data. Traditional centralized KDD typically requires central aggregation of distributed data, which may not always be feasible because of limited network bandwidth, security concerns, scalability problems, and other practical issues. Distributed knowledge discovery (DKD) works with the merger of communication and computation by analyzing data in a distributed fashion. This technology is particularly useful for large heterogeneous distributed environments such as the Internet, intranets, mobile computing environments, and sensor-networks.When the data sets are large, scaling up the speed of the KDD process is crucial. Parallel knowledge discovery (PKD) techniques addresses this problem by using high-performance multiprocessor machines. This book presents introductions to DKD and PKD, extensive reviews of the field, and state-of-the-art techniques. Contributors Rakesh Agrawal, Khaled AlSabti, Stuart Bailey, Philip Chan, David Cheung, Vincent Cho, Joydeep Ghosh, Robert Grossman, Yi-ke Guo, John Hale, John Hall, Daryl Hershberger, Ching-Tien Ho, Erik Johnson, Chris Jones, Chandrika Kamath, Hillol Kargupta, Charles Lo, Balinder Malhi, Ron Musick, Vincent Ng, Byung-Hoon Park, Srinivasan Parthasarathy, Andreas Prodromidis, Foster Provost, Jian Pun, Ashok Ramu, Sanjay Ranka, Mahesh Sreenivas, Salvatore Stolfo, Ramesh Subramonian, Janjao Sutiwaraphun, Kagan Tummer, Andrei Turinsky, Beat Wüthrich, Mohammed Zaki, Joshua Zhang
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Author: Pang-Ning Tan
Author: Richard J. Roiger
Author: R.L. Grossman
Author: Usama M. Fayyad
Author: Olivia Parr Rud
Author: Dorian Pyle
Author: Hillol Kargupta