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B01=Denis Hamad
B01=Fadi Dornaika
B01=Joseph Constantin
B01=Truong Hoang Vinh
Category1=Non-Fiction
Category=UMZ
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Category=UNF
Category=UYQE
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Advances in Data Clustering: Theory and Applications

English

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.

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Original price €162.99
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Age Group_Uncategorizedautomatic-updateB01=Denis HamadB01=Fadi DornaikaB01=Joseph ConstantinB01=Truong Hoang VinhCategory1=Non-FictionCategory=UMZCategory=UNCategory=UNFCategory=UYQECategory=UYQVCOP=SingaporeDelivery_Pre-orderLanguage_EnglishPA=Not yet availablePrice_€100 and abovePS=Forthcomingsoftlaunch

Will deliver when available. Publication date 04 Dec 2024

Product Details
  • Dimensions: 155 x 235mm
  • Publication Date: 04 Dec 2024
  • Publisher: Springer Verlag Singapore
  • Publication City/Country: Singapore
  • Language: English
  • ISBN13: 9789819776788

About

Fadi Dornaika   Fadi Dornaika got his PhD degree from INRIA France in 1995. He worked at several international research institutes in Europe Canada and China. He is currently an Ikerbasque research professor at the University of the Basque Country Spain. His research interests cover a broad spectrum in computer vision pattern recognition and machine learning. His current research interests include manifold learning multiview clustering scalable graph-based semi-supervised learning and deep learning. According to Stanford Universitys current ranking he is in the top 2% of scholars based on his citations on career-long data updated to end-of-2022 and single year (2022) impact (DOI:10.17632/btchxktzyw.6). He has published more than 380 papers in the field of computer vision pattern recognition and machine learning including 150 indexed journal articles in (IEEE Trans. Robotics and Automation IEEE Trans. Cybernetics IEEE Trans. Neural Networks and Learning Systems IEEE Trans. CSVT IEEE Trans. SMC Information Fusion Information Sciences Neural Networks Pattern Recognition Artificial Intelligence Review Knowledge-Based Systems International Journal of Computer Vision International Journal of Robotics Research etc.). Denis Hamad   Denis Hamad received the HDR (Habilitation to Supervise Research) degree in neural networks for unsupervised pattern classification and the Ph.D. degree in Validation of measurements and detection of faulty sensors in a control system from the Lille University France in 1997 and 1986 respectively. From 1998 to 2002 he was a professor with the University of Picardie Jules Vernes France. Since 2002 he holds a position as a professor with the University of the Littoral Opal Coast France and currently he is Professor Emeritus. His research interests include machine learning image processing and signal processing with applications to transportation management biomedical engineering and marine environmental management.     Joseph Constantin   Joseph Constantin received his bachelors and masters degrees in computer sciences from the Lebanese university and an additional masters degree in mathematical modelling and scientific software engineering from the Francophone university Agency (AUF) in 1997. He earned his Ph.D. in Automatic and Robotic control from the Picardie Jules Verne University France in 2000. Between 2001 and 2019 he was an associate professor at the Lebanese University and a researcher in the Applied Physics Laboratory of the Doctoral School of Sciences and Technology at the Lebanese University. Currently he is a full professor at the Lebanese University Faculty of Sciences and a researcher in the Research Laboratory in Networks Computer Science and Security (LaRRIS). He is doing research in collaboration with several international universities such as ULCO UTBM and Ho Chi Minh. Also he is working as a researcher and a professor at Saint Joseph University High school of Engineering (ESIB) Antonine University and Sagesse Polytech Faculty of Engineering. His current research interests are in the fields of clustering algorithms and traffic control deep learning kernel methods theory of automata and compiler design computer graphics and image processing robot dynamics and control diagnosis medical systems and biomedical engineering. Vinh Truong Hoang   Vinh Truong Hoang received his masters degree from the University of Montpellier and his Ph.D. degree from the University of the Littoral Opal Coast France. Currently he serves as an assistant professor at Ho Chi Minh City Open University Vietnam and holds the position of dean of the Faculty of Information Technology. His current research interests encompass machine learning deep learning clustering and computer vision with applications in intelligent systems climate change and biomedical fields. He conducts research in collaboration with several international universities such as SSRU UPES UPV and UDB.  

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