Temporal Data Mining via Unsupervised Ensemble Learning

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

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