Unsupervised Learning Algorithms

1. Auflage 2016
ISBN: 978-3-319-24209-5
Verlag: Springer International Publishing

Buch, Englisch, 558 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1010 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Aydin, Kemal

Celebi, M. Emre

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen

Inhaltsverzeichnis

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