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.
Table of Contents
Knowledge Discovery from Data Streams
An Illustrative Example
A World in Movement
Data Mining and Data Streams
Introduction to Data Streams
Data Stream Models
Basic Streaming Methods
Tracking Drifting Concepts
Monitoring the Learning Process
Maintaining Histograms from Data Streams
Histograms from Data Streams
The Partition Incremental Discretization (PiD) Algorithm
Applications to Data Mining
Evaluating Streaming Algorithms
Learning from Data Streams
Lessons Learned and Open Issues
Clustering from Data Streams
Frequent Pattern Mining
Introduction to Frequent Itemset Mining
Mining Frequent Itemsets from Data Streams
Sequence Pattern Mining
Decision Trees from Data Streams
The Very Fast Decision Tree Algorithm
Extensions to the Basic Algorithm
OLIN: Info-Fuzzy Algorithms
Novelty Detection in Data Streams
Learning and Novelty
Novelty Detection as a One-Class Classification Problem
Learning New Concepts
The Online Novelty and Drift Detection Algorithm
Ensembles of Classifiers
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
The Next Generation of Knowledge Discovery
Where We Want to Go
Notes appear at the end of each chapter.
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.
… 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