Introduction to Data Mining and Envelopment
Author | : Christopher J. Olmeda |
Publisher | : |
Total Pages | : |
Release | : 2003-03-01 |
Genre | : |
ISBN | : 9780982144237 |
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Author | : Christopher J. Olmeda |
Publisher | : |
Total Pages | : |
Release | : 2003-03-01 |
Genre | : |
ISBN | : 9780982144237 |
Author | : Pang-Ning Tan |
Publisher | : |
Total Pages | : 760 |
Release | : 2016 |
Genre | : |
ISBN | : 9789332571402 |
Author | : S. Sumathi |
Publisher | : Springer Science & Business Media |
Total Pages | : 836 |
Release | : 2006-09-26 |
Genre | : Computers |
ISBN | : 3540343504 |
This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in data base systems and new data base applications and is also designed to give a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, AI, machine learning, NN, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization. This book is intended for a wide audience of readers who are not necessarily experts in data warehousing and data mining, but are interested in receiving a general introduction to these areas and their many practical applications. Since data mining technology has become a hot topic not only among academic students but also for decision makers, it provides valuable hidden business and scientific intelligence from a large amount of historical data. It is also written for technical managers and executives as well as for technologists interested in learning about data mining.
Author | : Pang-Ning Tan |
Publisher | : Pearson UK |
Total Pages | : 866 |
Release | : 2019-03-04 |
Genre | : Computers |
ISBN | : 0273775324 |
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 organised into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you will receive via email the code and instructions on how to access this product. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed.
Author | : Chaitanya P Agrawal, Meena Agrawal |
Publisher | : Educreation Publishing |
Total Pages | : 114 |
Release | : |
Genre | : Self-Help |
ISBN | : |
This book is a small endeavor to share the journey of getting introduced to a wonderful topic Data Mining. Personally we came across this during the process of evaluating new tools to be included in the post graduate study curricula of the University we are working in. Soon it became a friendly affair to see the power, potential and ease of empowering the databases with concepts of data mining. It has become powerful in rediscovering the hidden values in data base and soon in data warehouse, equally efficiently. The Data mining is a powerful new technology with great potential focusing on the most important information in their data warehouses. It involves extraction of hidden predictive information from large databases with ease and efficiency. It facilitates to make proactive, knowledge-driven decisions and predict future trends and behaviors. Data mining tools move beyond the analyses of past events provided by retrospective tools typical of decision support systems. The automated, prospective analyses offered by data mining tools can answer finding predictive information easily. This small book is an introduction to the basics of data mining. It also introduces the techniques and technologies behind data mining, the impact of artificial intelligence, artificial neural networks, and fuzzy logic et cetera as the basic building blocks for the same. It concludes with common practical applications, trends and its impact on social and computing environment.
Author | : Kris Jamsa |
Publisher | : Jones & Bartlett Learning |
Total Pages | : 687 |
Release | : 2020-02-03 |
Genre | : Computers |
ISBN | : 1284180905 |
Data Mining and Analytics provides a broad and interactive overview of a rapidly growing field. The exponentially increasing rate at which data is generated creates a corresponding need for professionals who can effectively handle its storage, analysis, and translation.
Author | : Daniel T. Larose |
Publisher | : John Wiley & Sons |
Total Pages | : 240 |
Release | : 2005-01-28 |
Genre | : Computers |
ISBN | : 0471687537 |
Learn Data Mining by doing data mining Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: * Data preprocessing and classification * Exploratory analysis * Decision trees * Neural and Kohonen networks * Hierarchical and k-means clustering * Association rules * Model evaluation techniques Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.
Author | : Pang-Ning Tan |
Publisher | : Pearson Education India |
Total Pages | : 800 |
Release | : |
Genre | : |
ISBN | : 9788131764633 |
Author | : Daniel T. Larose |
Publisher | : John Wiley & Sons |
Total Pages | : 336 |
Release | : 2014-07-08 |
Genre | : Computers |
ISBN | : 0470908742 |
The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website for university instructors who adopt the book
Author | : Igor Kononenko |
Publisher | : Horwood Publishing |
Total Pages | : 484 |
Release | : 2007-04-30 |
Genre | : Computers |
ISBN | : 9781904275213 |
Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. Written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining, this text is suitable foradvanced undergraduates, postgraduates and tutors in a wide area of computer science and technology, as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to libraries and bookshelves of the many companies who are using the principles of data mining to effectively deliver solid business and industry solutions.