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.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. Temporal Data Mining - p. 9 3. Chapter 3. Temporal Data Clustering - p. 19 4. Chapter 4. Ensemble Learning - p. 35 5. Chapter 5. HMM-Based Hybrid Meta-Clustering in Association With Ensemble Technique - p. 57 6. Chapter 6. Unsupervised Learning via an Iteratively Constructed Clustering Ensemble - p. 75 7. Chapter 7. Temporal Data Clustering via a Weighted Clustering Ensemble With Different Representations - p. 93 8. Chapter 8. Conclusions, Future Work - p. 123 9. Appendix - p. 129 10. References - p. 147 11. Index - p. 155
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.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. Temporal Data Mining - p. 9 3. Chapter 3. Temporal Data Clustering - p. 19 4. Chapter 4. Ensemble Learning - p. 35 5. Chapter 5. HMM-Based Hybrid Meta-Clustering in Association With Ensemble Technique - p. 57 6. Chapter 6. Unsupervised Learning via an Iteratively Constructed Clustering Ensemble - p. 75 7. Chapter 7. Temporal Data Clustering via a Weighted Clustering Ensemble With Different Representations - p. 93 8. Chapter 8. Conclusions, Future Work - p. 123 9. Appendix - p. 129 10. References - p. 147 11. Index - p. 155
