Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more. This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses. Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods Performance improvement techniques that work by transforming the input or output
1.4 DATA: PROBABILISTIC VIEW The probabilistic view of the data assumes that each numeric attribute X is a random variable, defined as a function that assigns a real number to each outcome of an experiment (i.e., some process of ...
This is the sixth version of this successful text, and the first using Python.
This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases.
The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem.
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 ...
Temporal data mining deals with the harvesting of useful information from temporal data.
This book can show you how. Let's start digging! Through an agreement with the Global Text Project, an electronic version of this text is available online at (http://globaltext.terry.uga.edu/books).
The book reviews major emerging trends in technology that are supporting current advancements in social networks, including data mining techniques and tools.
This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas.
This third edition of Data Mining Techniques covers such topics as: How to create stable, long-lasting predictive models Data preparation and variable selection Modeling specific targets with directed techniques such as regression, decision ...