國立高雄大學圖資館 |

Language: English

Back

Advances in data clusteringtheory and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advances in data clusteringedited by Fadi Dornaika ... [et al.].
Reminder of title:	theory and applications /
other author:	Dornaika, Fadi.
Published:	Singapore :Springer Nature Singapore :2024.
Description:	xiv, 217 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Cluster analysis.
Online resource:	https://doi.org/10.1007/978-981-97-7679-5
ISBN:	9789819776795$q(electronic bk.)

Advances in data clusteringtheory and applications /
Advances in data clusteringtheory and applications /[electronic resource] :edited by Fadi Dornaika ... [et al.]. - Singapore :Springer Nature Singapore :2024. - xiv, 217 p. :ill. (chiefly color), digital ;24 cm.

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.

ISBN: 9789819776795$q(electronic bk.)

Standard No.: 10.1007/978-981-97-7679-5doiSubjects--Topical Terms:

182711
Cluster analysis.

LC Class. No.: QA278

Dewey Class. No.: 519.53

Advances in data clusteringtheory and applications /
LDR:03453nmm a2200349 a 4500 001 673511
003 DE-He213
005 20241230115250.0
006 m d
007 cr nn 008maaau
008 250422s2024 si s 0 eng d
020 $a 9789819776795$q(electronic bk.)
020 $a 9789819776788$q(paper)
024 7 $a 10.1007/978-981-97-7679-5 $2 doi
035 $a 978-981-97-7679-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA278
072 7 $a UNF $2 bicssc
072 7 $a UYQE $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 519.53 $2 23
090 $a QA278 $b .A244 2024
245 0 0 $a Advances in data clustering $h [electronic resource] : $b theory and applications / $c edited by Fadi Dornaika ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a xiv, 217 p. : $b ill. (chiefly color), digital ; $c 24 cm.
505 0 $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.
520 $a 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.
650 0 $a Cluster analysis. $3 182711
650 1 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Data Science. $3 913495
650 2 4 $a Information Model. $3 924168
650 2 4 $a Computer Vision. $3 913138
700 1 $a Dornaika, Fadi. $3 840858
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-97-7679-5
950 $a Mathematics and Statistics (SpringerNature-11649)