Albalate A., Minker W. Semi-Supervised and Unsupervised Machine Learning. Novel Strategies

ISTE/John Wiley, 2010. — 245 p.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.Part 1 State of the ArtState of the Art in Clustering and Semi-Supervised Techniques
Part 2 Approaches to Semi-Supervised Classification
Semi-Supervised Classification Using Prior Word Clustering
Semi-Supervised Classification Using Pattern Clustering
Part 3 Contributions to Unsupervised Classification – Algorithms to Detect the Optimal Number of Clusters
Detection of the Number of Clusters through Non-Parametric Clustering Algorithms
Detecting the Number of Clusters through Cluster Validation