Gama Knowledge Discovery from Data Streams

1. Auflage 2010
ISBN: 978-1-4398-2612-6
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 255 Seiten

Reihe: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

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.

Zielgruppe

Researchers and graduate students in data mining, machine learning, statistics, AI, and business intelligence.

Autoren/Hrsg.

Gama, Joao

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen

Inhaltsverzeichnis

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.

Über Autor(innen)

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.