Semi-Supervised and Unsupervised Machine Learning

Novel Strategies

Inbunden, Engelska, 2010

Av Amparo Albalate, Wolfgang Minker, Germany) Albalate, Amparo (University of Ulm, Institute of Information Technology, Germany) Minker, Wolfgang (University of Ulm, Institute of Information Technology

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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.

Produktinformation

Utgivningsdatum2010-12-14
Mått163 x 241 x 20 mm
Vikt508 g
FormatInbunden
SpråkEngelska
Antal sidor320
FörlagISTE Ltd and John Wiley & Sons Inc
ISBN9781848212039

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PART 1. STATE OF THE ART 1Chapter 1. Introduction 31.1. Organization of the book 61.2. Utterance corpus 81.3. Datasets from the UCI repository101.4. Microarray dataset 131.5. Simulated datasets 14Chapter 2. State of the Art in Clustering and Semi-Supervised Techniques 152.1. Introduction 152.2. Unsupervised machine learning (clustering) 152.3. A brief history of cluster analysis 162.4. Cluster algorithms 192.5. Applications of cluster analysis 522.6. Evaluation methods 772.7. Internal cluster evaluation 772.8. External cluster validation 802.9. Semi-supervised learning 842.10. Summary 88PART 2. APPROACHES TO SEMI-SUPERVISED CLASSIFICATION 91Chapter 3. Semi-Supervised Classification Using Prior Word Clustering 933.1. Introduction 933.2. Dataset 943.3. Utterance classification scheme 943.4. Semi-supervised approach based on term clustering 983.5. Disambiguation 1133.6. Summary 124Chapter 4. Semi-Supervised Classification Using Pattern Clustering 1274.1. Introduction 1274.2. New semi-supervised algorithm using the cluster and label strategy 1284.3. Optimum cluster labeling 1324.4. Supervised classification block 1544.5. Datasets 1594.6. An analysis of the bounds for the cluster and label approaches 1624.7. Extension through cluster pruning 1644.8. Simulations and results 1734.9. Summary 179PART 3 . CONTRIBUTIONS TO UNSUPERVISED CLASSIFICATION – ALGORITHMS TO DETECT THE OPTIMAL NUMBER OF CLUSTERS 183Chapter 5. Detection of the Number of Clusters through Non-Parametric Clustering Algorithms 1855.1. Introduction 1855.2. New hierarchical pole-based clustering algorithm 1865.3. Evaluation 1905.4. Datasets 1925.5. Summary 197Chapter 6. Detecting the Number of Clusters through Cluster Validation 1996.1. Introduction 1996.2. Cluster validation methods 2016.3. Combination approach based on quantiles 2066.4. Datasets 2126.5. Results 2146.6. Application of speech utterances 2236.7. Summary 224Bibliography 227Index 243