LEADER 04395nam 22006615 450 001 9910920445903321 005 20251113182008.0 010 $a9789819776795 010 $a9819776791 024 7 $a10.1007/978-981-97-7679-5 035 $a(MiAaPQ)EBC31867310 035 $a(Au-PeEL)EBL31867310 035 $a(CKB)37111016700041 035 $a(DE-He213)978-981-97-7679-5 035 $a(OCoLC)1492926325 035 $a(EXLCZ)9937111016700041 100 $a20241230d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Data Clustering $eTheory and Applications /$fedited by Fadi Dornaika, Denis Hamad, Joseph Constantin, Vinh Truong Hoang 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (225 pages) 311 08$a9789819776788 311 08$a9819776783 327 $a 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. 330 $aClustering, 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. 606 $aData mining 606 $aArtificial intelligence$xData processing 606 $aInformation modeling 606 $aComputer vision 606 $aData Mining and Knowledge Discovery 606 $aData Science 606 $aInformation Model 606 $aComputer Vision 615 0$aData mining. 615 0$aArtificial intelligence$xData processing. 615 0$aInformation modeling. 615 0$aComputer vision. 615 14$aData Mining and Knowledge Discovery. 615 24$aData Science. 615 24$aInformation Model. 615 24$aComputer Vision. 676 $a006.312 700 $aDornaika$b Fadi$01780903 701 $aHamad$b Denis$01781727 701 $aConstantin$b Joseph$01781728 701 $aHoang$b Vinh Truong$01781729 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910920445903321 996 $aAdvances in Data Clustering$94306553 997 $aUNINA