Adaptive Stream Mining Pattern Learning and Mining from Evolving Data Streams Volume 207 Frontiers in Artificial Intelligence and Applications 1st Edition by Albert Bifet ISBN 1607500906 9781607500902

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Product details:
ISBN 10: 1607500906
ISBN 13: 9781607500902
Author: Albert Bifet

This book is a significant contribution to the subject of mining time-changing data streams and addresses the design of learning algorithms for this purpose. It introduces new contributions on several different aspects of the problem, identifying research opportunities and increasing the scope for applications. It also includes an in-depth study of stream mining and a theoretical analysis of proposed methods and algorithms. The first section is concerned with the use of an adaptive sliding window algorithm (ADWIN). Since this has rigorous performance guarantees, using it in place of counters or accumulators, it offers the possibility of extending such guarantees to learning and mining algorithms not initially designed for drifting data. Testing with several methods, including Naïve Bayes, clustering, decision trees and ensemble methods, is discussed as well. The second part of the book describes a formal study of connected acyclic graphs, or trees, from the point of view of closure-based mining, presenting efficient algorithms for subtree testing and for mining ordered and unordered frequent closed trees. Lastly, a general methodology to identify closed patterns in a data stream is outlined. This is applied to develop an incremental method, a sliding-window based method, and a method that mines closed trees adaptively from data streams. These are used to introduce classification methods for tree data streams.

Adaptive Stream Mining Pattern Learning and Mining from Evolving Data Streams Volume 207 Frontiers in Artificial Intelligence and Applications 1st Edition Table of contents:

Chapter 1: Introduction to Data Streams

• Understanding Data Streams and Their Characteristics
• Challenges in Data Stream Mining
• Importance of Adaptivity in Stream Mining

Chapter 2: Fundamentals of Stream Mining

• What is Stream Mining?
• Theoretical Foundations of Stream Mining Algorithms
• Types of Data Streams: Static vs. Evolving

Chapter 3: Stream Data Models

• Real-time Data and its Characteristics
• Continuous vs. Discrete Streams
• Representation of Data in Streams

Chapter 4: Algorithms for Stream Mining

• Basic Algorithms for Stream Processing
• Single-pass Algorithms
• Sliding Window Algorithms

Chapter 5: Adaptive Learning from Evolving Streams

• Importance of Adaptivity in Evolving Data
• Techniques for Handling Concept Drift
• Incremental Learning Models

Chapter 6: Classification and Regression in Data Streams

• Stream-Based Classification Algorithms
• Adapting to Changes in Data Distribution
• Regression Models for Stream Mining

Chapter 7: Clustering in Evolving Data Streams

• Streaming Clustering Techniques
• Handling Noise and Outliers in Streams
• Density-based Clustering for Evolving Data

Chapter 8: Anomaly Detection and Outlier Mining

• Identifying Anomalies in Data Streams
• Techniques for Detecting Novelty and Drifting Patterns
• Real-world Applications of Anomaly Detection

Chapter 9: Mining Sequential Patterns from Streams

• Temporal and Sequential Pattern Mining
• Algorithms for Sequential Stream Mining
• Case Studies of Sequential Pattern Applications

Chapter 10: Evaluation of Stream Mining Algorithms

• Performance Metrics for Data Stream Mining
• Benchmarking Stream Mining Techniques
• Practical Challenges and Solutions

Chapter 11: Real-World Applications of Stream Mining

• Applications in Finance, Health, and Security
• Case Studies: Real-time Data Processing
• Future Trends in Stream Mining and Adaptivity

Chapter 12: Challenges and Future Directions

• Open Problems in Stream Mining
• The Role of Big Data and Cloud Computing
• The Future of Adaptive Learning in Evolving Data Streams

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