Advances data clustering theory (6 Ergebnisse)

hardcover. Zustand: Good. Some light external wear. Pages are clean, text and pictures are intact and unmarred. Binding intact and firm. Good reading copy.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Advances in Data Clustering | Theory and Applications | Fadi Dornaika (u. a.) | Taschenbuch | xiv | Englisch | 2025 | Springer | EAN 9789819776818 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Hardcover. Zustand: Brand New. 210 pages. 9.25x6.10x9.21 inches. In Stock.