04705nam a22006015i 4500 978-981-97-7679-5 DE-He213 20251030114651.0 cr nn 008mamaa 241230s2024 si | s |||| 0|eng d 9789819776795 978-981-97-7679-5 10.1007/978-981-97-7679-5 doi QA76.9.D343 UNF bicssc UYQE bicssc COM021030 bisacsh UNF thema UYQE thema Advances in Data Clustering [electronic resource] : Theory and Applications / edited by Fadi Dornaika, Denis Hamad, Joseph Constantin, Vinh Truong Hoang. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XIV, 217 p. 49 illus., 45 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Chapter 1 Classification of Gougerot-Sjögren syndrome Based on Artificial Intelligence -- Chapter 2 Deep learning Classification of Venous Thromboembolism based on Ultrasound imaging -- Chapter 3 Synchronization-Driven Community Detection: Dynamic Frequency Tuning Approach -- Chapter 4 Automatic Evolutionary Clustering for Human Activity Discovery -- Chapter 5 Identification of Correlated factors for Absenteeism of employees using Clustering techniques -- Chapter 6 Multi-view Data Clustering through Consensus Graph and Data Representation Learning -- Chapter 7 Uber’s Contribution to Faster Deep Learning: A Case Study in Distributed Model Training -- Chapter 8 Auto-Weighted Multi-View Clustering with Unified Binary Representation and Deep Initialization -- Chapter 9 Clustering with Adaptive Unsupervised Graph Convolution Network -- Chapter 10 Graph-based Semi-supervised Learning for Multi-view Data Analysis -- Chapter 11 Advancements in Fuzzy Clustering Algorithms for Im-age Processing: A Comprehensive Review and Future Directions -- Chapter 12 Multiview Latent representation learning with feature diversity for clustering. 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. Data mining. 6941 Artificial intelligence Data processing. Information modeling. 81621 Computer vision. Data Mining and Knowledge Discovery. Data Science. Information Model. 81622 Computer Vision. Dornaika, Fadi. editor. edt http://id.loc.gov/vocabulary/relators/edt Hamad, Denis. editor. edt http://id.loc.gov/vocabulary/relators/edt 89909 Constantin, Joseph. editor. edt http://id.loc.gov/vocabulary/relators/edt 89910 Hoang, Vinh Truong. editor. edt http://id.loc.gov/vocabulary/relators/edt 89911 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819776788 Printed edition: 9789819776801 Printed edition: 9789819776818 https://doi.org/10.1007/978-981-97-7679-5 ZDB-2-SMA ZDB-2-SXMS 66206 66206