Presenting the underlying matrix theory and explaining its effectiveness as a tool, this work demonstrates how to build models of complex data using real-world scientific and engineering systems, and focuses on a range of applications such as information retrieval, topic detection, and social network analysis.
Presenting the underlying matrix theory and explaining its effectiveness as a tool, this work demonstrates how to build models of complex data using r...
Data clustering is a highly interdisciplinary field, the goal of which is to divide a set of objects into homogeneous groups such that objects in the same group are similar and objects in different groups are quite distinct. Thousands of theoretical papers and a number of books on data clustering have been published over the past 50 years. However, few books exist to teach people how to implement data clustering algorithms. This book was written for anyone who wants to implement or improve their data clustering algorithms. Using object-oriented design and programming techniques, Data...
Data clustering is a highly interdisciplinary field, the goal of which is to divide a set of objects into homogeneous groups such that objects in the ...
Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified framework for supervised, unsupervised, and semisupervised feature selection.
The book explores the latest research achievements, sheds light on new research directions, and stimulates readers to make the next creative breakthroughs. It presents the intrinsic ideas...
Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for ...
Machine Learning and Knowledge Discovery for Engineering Systems Health Management presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events in an engineered system. With contributions from many top authorities on the subject, this volume is the first to bring together the two areas of machine learning and systems health management.
Divided into three parts, the book explains how the fundamental algorithms and methods of both physics-based and data-driven approaches effectively...
Machine Learning and Knowledge Discovery for Engineering Systems Health Management presents state-of-the-art tools and techniques ...
This book provides a comprehensive overview of various data mining tools and techniques that are increasingly being used by researchers in the international astronomy community. It explores this new problem domain, discussing how it could lead to the development of entirely new algorithms. Leading contributors introduce data mining methods and then describe how the methods can be implemented into astronomy applications. The last section of the book discusses the Redshift Prediction Competition, which is an astronomy competition in the style of the Netflix Prize.
This book provides a comprehensive overview of various data mining tools and techniques that are increasingly being used by researchers in the inte...
"Dr. Roiger does an excellent job of describing in step by step detail formulae involved in various data mining algorithms, along with illustrations. In addition, his tutorials in Weka software provide excellent grounding for students in comprehending the underpinnings of Machine Learning as applied to Data Mining. The inclusion of RapidMiner software tutorials and examples in the book is also a definite plus since it is one of the most popular Data Mining software platforms in use today."
--Robert Hughes, Golden Gate University, San Francisco,...
"Dr. Roiger does an excellent job of describing in step by step detail formulae involved in various data mining algorithms, along with illustra...
This textbook introduces exploratory data analysis (EDA) and covers the range of interesting features we can expect to find in data. The book also explores the practical mechanics of using R to do EDA. Based on the author's course at the University of Connecticut, the book assumes no prior exposure to data analysis or programming, and is designed to be as non-mathematical as possible. Exercises are included throughout, and a Solutions Manual will be available. The author will also provide a supplemental R package through the Comprehensive R Archive Network that will include implementations...
This textbook introduces exploratory data analysis (EDA) and covers the range of interesting features we can expect to find in data. The book also ...
