Book description
This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer subtypes in genomic microarray data. Among those, the book focuses on a new application for dialog systems which can be thereby made adaptable and portable to different domains. Clustering evaluation metrics and new approaches, such as the ensembles of clustering algorithms, are also described.
Table of contents
- Cover
- Title Page
- Copyright
-
Part 1: State of the Art
- Chapter 1: Introduction
-
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
- 2.5.1. Image segmentation
- 2.5.2. Molecular biology
- 2.5.3. Information retrieval and document clustering
- 2.5.4. Clustering documents in information retrieval
- 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
-
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
- Chapter 6: Detecting the Number of Clusters through Cluster Validation
- Bibliography
- Index
Product information
- Title: Semi-Supervised and Unsupervised Machine Learning: Novel Strategies
- Author(s):
- Release date: January 2011
- Publisher(s): Wiley
- ISBN: 9781848212039
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