Advances in data clustering : theory and applications / Fadi Dornaika ... [et al.] editors

• Pubblicazione: Singapore, : Springer, 2024
• Descrizione fisica: 1 testo elettronico (xiv, 217 p. : ill.)
• Soggetto topico: 62H30 - Classification and discrimination; cluster analysis (statistical aspects) [MSC 2020]; 68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020]; 68T10 - Pattern recognition, speech recognition [MSC 2020]
• Soggetto non controllato: Data clustering; Deep Learning; Graph neural networks; Nongraph data; Unsupervised Learning
• Persona (resp. second.): Dornaika, Fadi
• Sommario/riassunto: 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. (Dal sito dell'editore)
• Titolo autorizzato: Advances in data clustering
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: VAN00310091
• Lo trovi qui: Univ. Vanvitelli
• Localizzazioni e accesso elettronico: https://doi.org/10.1007/978-981-97-7679-5