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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
Undergraduate and graduate students who major in machine learning and data mining. Scientists, researchers and data analysts working on temporal data mining, ensemble learning
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
- 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
- No. of pages:
- © Elsevier 2017
- 18th November 2016
- Paperback ISBN:
- eBook ISBN:
Dr Yang has a very solid and broad knowledge and experience in computer science, and in-depth expertise in machine learning, data mining and temporal data processing. His main research area is in the temporal data mining and unsupervised ensemble learning. In these topics, he has produced some internationally excellent research results including proposing and developing several innovation methods and algorithms. These works have been published in the international leading research journals or conferences such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Systems, Man, and Cybernetics- Part C, and Knowledge-Based Systems. His research results have attracted a lot of attentions from the machine learning research community and made the significant impact. As an evidence to illustrate the attention that his work has received and the impact his work has produced, his IEEE Transaction publication “Temporal data clustering via weighted clustering ensemble with different representations” has been cited more than 42 times based on Google scholar.
School of Software, Yunnan University, China
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