Knowledge Discovery from Data Streams

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.