
Temporal Data Mining via Unsupervised Ensemble Learning
Description
Key Features
- 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
Readership
Undergraduate and graduate students who major in machine learning and data mining. Scientists, researchers and data analysts working on temporal data mining, ensemble learning
Table of Contents
Chapter 1. Introduction
- 1.1. Background
- 1.2. Problem Statement
- 1.3. Objective of Book
- 1.4. Overview of Book
Chapter 2. Temporal Data Mining
- 2.1. Introduction
- 2.2. Representations of Temporal Data
- 2.3. Similarity Measures
- 2.4. Mining Tasks
- 2.5. Summary
Chapter 3. Temporal Data Clustering
- 3.1. Introduction
- 3.2. Overview of Clustering Algorithms
- 3.3. Clustering Validation
- 3.4. Summary
Chapter 4. Ensemble Learning
- 4.1. Introduction
- 4.2. Ensemble Learning Algorithms
- 4.3. Combining Methods
- 4.4. Diversity of Ensemble Learning
- 4.5. Clustering Ensemble
- 4.6. Summary
Chapter 5. HMM-Based Hybrid Meta-Clustering in Association With Ensemble Technique
- 5.1. Introduction
- 5.2. HMM-Based Hybrid Meta-Clustering Ensemble
- 5.3. Simulation
- 5.4. Summary
Chapter 6. Unsupervised Learning via an Iteratively Constructed Clustering Ensemble
- 6.1. Introduction
- 6.2. Iteratively Constructed Clustering Ensemble
- 6.3. Simulation
- 6.4. Summary
Chapter 7. Temporal Data Clustering via a Weighted Clustering Ensemble With Different Representations
- 7.1. Introduction
- 7.2. Weighted Clustering Ensemble With Different Representations of Temporal Data
- 7.3. Simulation
- 7.4. Summary
Chapter 8. Conclusions, Future Work
Appendix
- A.1. Weighted Clustering Ensemble Algorithm Analysis
- A.2. Implementation of HMM-Based Meta-clustering Ensemble in Matlab Code
- A.3. Implementation of Iteratively Constructed Clustering Ensemble in Matlab Code
- A.4. Implementation of WCE With Different Representations
Product details
- No. of pages: 172
- Language: English
- Copyright: © Elsevier 2016
- Published: November 15, 2016
- Imprint: Elsevier
- Paperback ISBN: 9780128116548
- eBook ISBN: 9780128118412
About the Author
Yun Yang
Affiliations and Expertise
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