Advances in Data Clustering: Theory and Applications

by Fadi Dornaika (Editor), Denis Hamad (Editor), Joseph Constantin (Editor), Vinh Truong Hoang (Editor)

Overview

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.

Product Details

ISBN-13:	9789819776788
Publisher:	Springer Nature Singapore
Publication date:	12/30/2024
Edition description:	2024
Pages:	217
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

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 University’s 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 bachelor’s and master’s degrees in computer sciences from the Lebanese university and an additional master’s 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 master’s 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.

Table of Contents

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.