This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology. The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining. This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources. Chapters contributed by various recognized experts in the field let the reader remain up to date and fully informed from multiple viewpoints. Presents multiple methods of analysis and algorithmic problem-solving techniques, enhancing the reader’s technical expertise and ability to implement practical solutions. Coverage of both theory and practice brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases.
P. S. Yu, J. Han, and C. Faloutsos. Link Mining: Models, Algorithms and Applications. New York: Springer, 2010. X. Yin, J. Han, and P. S. Yu. Cross-relational clustering with user's guidance. In Proc. 2005 ACM SIGKDD Int. Conf.
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 -- ...
Imhoff, C., Sousa, R., 1997. The Information Ecosystem. Part 1. DM-Review, January 27. Inmon, W., Imhoff, C., Sousa, R., 1998. Corporate Information Factory. John Wiley & Sons, New York, NY. Juran, J., 1951. Quality Control Handbook.
Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining.
This book focuses on the importance of clean, well-structured data as the first step to successful data mining.
[21] J. Yang, J. McAuley, J. Leskovec, “Detecting cohesive and 2-mode communities in directed and undirected networks,” ACM Intl. Conf. on Web Search and Data Mining, 2014. [22] J. Yang, J. McAuley, J. Leskovec, “Community detection in ...
This guide also helps you understand the many data-mining techniques in use today.
An Instructor's Manual presenting detailed solutions to all the problems in the book is available online. Learn Data Mining by doing data mining Data mining can be revolutionary—but only when it's done right.
This book can show you how. Let's start digging! Author's Note: The first edition of this text continues to be available for download, free of charge as a PDF file, from the GlobalText online library.
Although her original intention for her dissertation research was to do a qualitative case-study based on organizational documents and interviews with clients as well as staff, her position as Executive Director raised Hunter-IRB ...