Gama J. Knowledge Discovery from Data Streams

Chapman & Hall/CRC Press, 2010. — 247 p.This book is the first book to didactically cover in a clear, comprehensive and mathematically rigorous way the main machine learning related aspects of this relevant research field. The book not only presents the main fundamentals important to fully understand data streams, but also describes important applications. The book also discusses some of the main challenges of data mining future research, when stream mining will be at the core of many applications. These challenges will have to be addressed for the design of useful and efficient data mining solutions able to deal with real-world problems. It is important to stress that, in spite of this book being mainly about data streams, most of the concepts presented are valid for different areas of machine learning and data mining. Therefore, the book is an up-to-date, broad and useful source of reference for all those interested in knowledge acquisition by learning techniques.Knowledge Discovery from Data Streams
Introduction to Data Streams
Change Detection
Maintaining Histograms from Data Streams
Evaluating Streaming Algorithms
Clustering from Data Streams
Frequent Pattern Mining
Decision Trees from Data Streams
Novelty Detection in Data Streams
Ensembles of Classifiers
Time Series Data Streams
Ubiquitous Data Mining
Final Comments
A: Resources