Table of Contents

Part 1. State of the Art

Chapter 1. Introduction

1.1. Organization of the book

1.2. Utterance corpus

1.3. Datasets from the UCI repository

1.4. Microarray dataset

1.5. Simulated datasets

Chapter 2. State of the Art in Clustering and Semi-Supervised Techniques

2.1. Introduction

2.2. Unsupervised machine learning (clustering)

2.3. A brief history of cluster analysis

2.4. Cluster algorithms

2.5. Applications of cluster analysis

2.6. Evaluation methods

2.7. Internal cluster evaluation

2.8. External cluster validation

2.9. Semi-supervised learning

2.10. Summary

Part 2. Approaches to Semi-Supervised Classification

Chapter 3. Semi-Supervised Classification Using Prior Word Clustering

3.1. Introduction

3.2. Dataset

3.3. Utterance classification scheme

3.4. Semi-supervised approach based on term clustering

3.5. Disambiguation

3.6. Summary

Chapter 4. Semi-Supervised Classification Using Pattern Clustering

4.1. Introduction

4.2. New semi-supervised algorithm using the cluster and label strategy

4.3. Optimum cluster labeling

4.4. Supervised classification block

4.5. Datasets

4.6. An analysis of the bounds for the cluster and label approaches

4.7. Extension through cluster pruning

4.8. Simulations and results

4.9. Summary

Part 3. Contributions to Unsupervised Classification — Algorithms to Detect the Optimal Number of Clusters

Chapter 5. Detection of the Number of Clusters through Non-Parametric Clustering Algorithms

5.1. Introduction

5.2. New hierarchical pole-based clustering ...

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