Unsupervised Learning Algorithms (eBook, PDF)

Redaktion: Celebi, M. Emre; Aydin, Kemal

• Format: PDF

Produktbeschreibung

This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest includeanomaly detection, clustering, feature extraction, and applications of unsupervised learning. Each chapter is contributed by a leading expert in the field.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 558
• Erscheinungstermin: 29. April 2016
• Englisch
• ISBN-13: 9783319242118
• Artikelnr.: 46972505

Inhaltsangabe

Introduction.- Feature Construction.- Feature Extraction.- Feature Selection.- Association Rule Learning.- Clustering.- Anomaly/Novelty/Outlier Detection.- Evaluation of Unsupervised Learning.- Applications.- Conclusion.

Rezensionen

"The book provides a valuable survey of an area of both research and application, particularly as massive datasets have become available. ... The book can be recommended to anyone interested in getting an overview of this fast-moving research and application area. Because each chapter has a comprehensive bibliography, the book can serve as an entry point for those wishing to work in or with unsupervised learning." (J. P. E. Hodgson, Computing Reviews, computingreviews.com, August, 2016)