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Temporal Data Mining via Unsupervised Ensemble Learning

Book Description

Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice.

Furthermore, the book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodologies, and is a guide to the proper usage of these methods. As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct approach can be selected for a given clustering problem.

Scientists, researchers, and data analysts working with machine learning and data mining will benefit from this innovative book, as will undergraduate and graduate students following courses in computer science, engineering, and statistics.

  • Includes fundamental concepts and knowledge, covering all key tasks and techniques of temporal data mining, i.e., temporal data representations, similarity measure, and mining tasks
  • Concentrates on temporal data clustering tasks from different perspectives, including major algorithms from clustering algorithms and ensemble learning approaches
  • Presents a rich blend of theory and practice, addressing seminal research ideas and looking at the technology from a practical point-of-view

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. List of Figures
  6. List of Tables
  7. Acknowledgments
  8. Chapter 1. Introduction
    1. 1.1. Background
    2. 1.2. Problem Statement
    3. 1.3. Objective of Book
    4. 1.4. Overview of Book
  9. Chapter 2. Temporal Data Mining
    1. 2.1. Introduction
    2. 2.2. Representations of Temporal Data
    3. 2.3. Similarity Measures
    4. 2.4. Mining Tasks
    5. 2.5. Summary
  10. Chapter 3. Temporal Data Clustering
    1. 3.1. Introduction
    2. 3.2. Overview of Clustering Algorithms
    3. 3.3. Clustering Validation
    4. 3.4. Summary
  11. Chapter 4. Ensemble Learning
    1. 4.1. Introduction
    2. 4.2. Ensemble Learning Algorithms
    3. 4.3. Combining Methods
    4. 4.4. Diversity of Ensemble Learning
    5. 4.5. Clustering Ensemble
    6. 4.6. Summary
  12. Chapter 5. HMM-Based Hybrid Meta-Clustering in Association With Ensemble Technique
    1. 5.1. Introduction
    2. 5.2. HMM-Based Hybrid Meta-Clustering Ensemble
    3. 5.3. Simulation
    4. 5.4. Summary
  13. Chapter 6. Unsupervised Learning via an Iteratively Constructed Clustering Ensemble
    1. 6.1. Introduction
    2. 6.2. Iteratively Constructed Clustering Ensemble
    3. 6.3. Simulation
    4. 6.4. Summary
  14. Chapter 7. Temporal Data Clustering via a Weighted Clustering Ensemble With Different Representations
    1. 7.1. Introduction
    2. 7.2. Weighted Clustering Ensemble With Different Representations of Temporal Data
    3. 7.3. Simulation
    4. 7.4. Summary
  15. Chapter 8. Conclusions, Future Work
  16. Appendix
    1. A.1. Weighted Clustering Ensemble Algorithm Analysis
    2. A.2. Implementation of HMM-Based Meta-clustering Ensemble in Matlab Code
    3. A.3. Implementation of Iteratively Constructed Clustering Ensemble in Matlab Code
    4. A.4. Implementation of WCE With Different Representations
  17. References
  18. Index