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Knowledge Discovery from Data Streams
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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.
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Table of Contents

Knowledge Discovery from Data Streams Introduction An Illustrative Example A World in Movement Data Mining and Data Streams Introduction to Data Streams Data Stream Models Basic Streaming Methods Illustrative Applications Change Detection Introduction Tracking Drifting Concepts Monitoring the Learning Process Final Remarks Maintaining Histograms from Data Streams Introduction Histograms from Data Streams The Partition Incremental Discretization (PiD) Algorithm Applications to Data Mining Evaluating Streaming Algorithms Introduction Learning from Data Streams Evaluation Issues Lessons Learned and Open Issues Clustering from Data Streams Introduction Clustering Examples Clustering Variables Frequent Pattern Mining Introduction to Frequent Itemset Mining Heavy Hitters Mining Frequent Itemsets from Data Streams Sequence Pattern Mining Decision Trees from Data Streams Introduction The Very Fast Decision Tree Algorithm Extensions to the Basic Algorithm OLIN: Info-Fuzzy Algorithms Novelty Detection in Data Streams Introduction Learning and Novelty Novelty Detection as a One-Class Classification Problem Learning New Concepts The Online Novelty and Drift Detection Algorithm Ensembles of Classifiers Introduction Linear Combination of Ensembles Sampling from a Training Set Ensembles of Trees Adapting to Drift Using Ensembles of Classifiers Mining Skewed Data Streams with Ensembles Time Series Data Streams Introduction to Time Series Analysis Time Series Prediction Similarity between Time Series Symbolic Approximation (SAX) Ubiquitous Data Mining Introduction to Ubiquitous Data Mining Distributed Data Stream Monitoring Distributed Clustering Algorithm Granularity Final Comments The Next Generation of Knowledge Discovery Where We Want to Go Appendix: Resources Bibliography Index Notes appear at the end of each chapter.

About the Author

Joao 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.

Reviews

"!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 Andre Ponce de Leon Ferreira de Carvalho, University of Sao Paulo, Brazil

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