Advances in Data Clustering: Theory and Applications - Couverture souple

Couverture souple

ISBN 10 : 9819776813 ISBN 13 : 9789819776818

Editeur : Springer, 2025

Synopsis

Clustering, a foundational technique in data analytics, finds diverse applications across scientific, technical, and business domains. Within the theme of “Data Clustering,” this book assumes substantial importance due to its indispensable clustering role in various contexts.

As the era of online media facilitates the rapid generation of large datasets, clustering emerges as a pivotal player in data mining and machine learning. At its core, clustering seeks to unveil heterogeneous groups within unlabeled data, representing a crucial unsupervised task in machine learning. The objective is to automatically assign labels to each unlabeled datum with minimal human intervention. Analyzing this data allows for categorization and drawing conclusions applicable across diverse application domains. The challenge with unlabeled data lies in defining a quantifiable goal to guide the model-building process, constituting the central theme of clustering.

This book presents concepts and different methodologies of data clustering. For example, deep clustering of images, semi-supervised deep clustering, deep multi-view clustering, etc. This book can be used as a reference for researchers and postgraduate students in related research background.

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2025
• Langue: anglais
• ISBN 10: 9819776813
• ISBN 13: 9789819776818
• Reliure: Broché
• Nombre de pages: 232
• Éditeur: Dornaika Fadi, Hamad Denis, Constantin Joseph, Hoang Vinh Truong
• Coordonnées du fabricant: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Allemagne