Knowledge Discovery from Data Streams

Exploring how to extract knowledge structures from evolving and time-changing data, this book presents a coherent overview of state-of-the-art research in learning from data streams. It covers the fundamentals that are imperative to understanding data streams and describes important applications, such as TCP/IP traffic, GPS data, sensor networks, and customer click streams. It also explores advanced areas, such as ubiquitous data stream mining; addresses several challenges of data mining in the future, when stream mining will be at the core of many applications; and includes pseudo-code of more than 30 streaming-like algorithms.

Zusatztext ? this book is the first authored text (that is! not an edited collection) about the area ? The book covers a lot of ground in just 200 pages! including discussion of relatively advanced methods such as wavelets! bagging! boosting! dynamic time warping! and symbolic representation of time series. There is also! I was pleased to see! a chapter on evaluating streaming algorithms ? . Evaluation! in general! deserves more attention than it generally receives! so I was delighted to see the focus on it here. ? a good introduction to an area of data analysis which is going to be very important indeed.-David J. Hand! International Statistical Review! 2012Gama is one of the leading investigators in the hottest research topic in machine learning and data mining: data streams. ? 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. ? an up-to-date! broad and useful source of reference for all those interested in knowledge acquisition by learning techniques.-From the Foreword by André Ponce de Leon Ferreira de Carvalho! University of São Paulo! Brazil Informationen zum Autor João Gama is an associate professor and senior researcher in the Laboratory of Artificial Intelligence and Decision Support (LIAAD) at the University of Porto in Portugal. Klappentext Exploring how to extract knowledge structures from evolving and time-changingdata, "Knowledge Discovery from Data Streams" presents a coherent overview ofstate-of-the-art research in learning from data streams. Zusammenfassung Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents a coherent overview of state-of-the-art research in learning from data streams. The book covers the fundamentals that are imperative to understanding data streams and describes important applications, such as TCP/IP traffic, GPS data, sensor networks, and customer click streams. It also addresses several challenges of data mining in the future, when stream mining will be at the core of many applications. These challenges involve designing useful and efficient data mining solutions applicable to real-world problems. In the appendix, the author includes examples of publicly available software and online data sets. This practical, up-to-date book focuses on the new requirements of the next generation of data mining. Although the concepts presented in the text are mainly about data streams, they also are valid for different areas of machine learning and data mining. Inhaltsverzeichnis 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. Appendix. Bibliography. Index. ...

this book is the first authored text (that is, not an edited collection) about the area The book covers a lot of ground in just 200 pages, including discussion of relatively advanced methods such as wavelets, bagging, boosting, dynamic time warping, and symbolic representation of time series. There is also, I was pleased to see, a chapter on evaluating streaming algorithms . Evaluation, in general, deserves more attention than it generally receives, so I was delighted to see the focus on it here. a good introduction to an area of data analysis which is going to be very important indeed.David J. Hand, International Statistical Review, 2012 Gama is one of the leading investigators in the hottest research topic in machine learning and data mining: data streams. 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. an up-to-date, broad and useful source of reference for all those interested in knowledge acquisition by learning techniques.From the Foreword by André Ponce de Leon Ferreira de Carvalho, University of São Paulo, Brazil

Autorentext

João Gama is an associate professor and senior researcher in the Laboratory of Artificial Intelligence and Decision Support (LIAAD) at the University of Porto in Portugal.

Zusammenfassung

Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents a coherent overview of state-of-the-art research in learning from data streams.

The book covers the fundamentals that are imperative to understanding data streams and describes important applications, such as TCP/IP traffic, GPS data, sensor networks, and customer click streams. It also addresses several challenges of data mining in the future, when stream mining will be at the core of many applications. These challenges involve designing useful and efficient data mining solutions applicable to real-world problems. In the appendix, the author includes examples of publicly available software and online data sets.

This practical, up-to-date book focuses on the new requirements of the next generation of data mining. Although the concepts presented in the text are mainly about data streams, they also are valid for different areas of machine learning and data mining.

Inhalt

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. Appendix. Bibliography. Index.