Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects Complete classroom support for instructors at www.mkp.com/datamining2e companion site
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 is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table ...
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.
The three-volume set LNAI 11439, 11440, and 11441 constitutes the thoroughly refereed proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, held in Macau, China, in April 2019.
Principal topics discussed throughout the text include: The role of soft computing and its principles in data mining Principles and classical algorithms on string matching and their role in data (mainly text) mining Data compression ...
If the outcomes are numeric, and represent the observed values of the random variable, then X: O → O is simply the identity function: X(v) = v for all v ∈ O. The distinction between the outcomes and the value of the random variable is ...
C.J. Merz, P.M. Murphy. UCI Repository of Machine Learning Databases. Irvine, CA: University of California, Department of Information and Computer Science, http://www.ics.uci.edu/~mlearn/MLOther.html. W. Michalowski, S. Rubin ...
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.
Spatial data mining and geographic knowledge discovery—An introduction, Elsevier, Computers. ... Multivariate Data Analysis. Pearson Education. Hall, D., Jurafsky, D., ... Data mining, southeast asia edition: Concepts and techniques.
Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining.