Knowledge Discovery from Data Streams

by Joao Gama

• ISBN13: 9781439826126
• ISBN10: 1439826129
• Format: Nonspecific Binding
• Copyright: 2010-05-25
• Publisher: Taylor & Francis

Summary

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

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

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