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5th International Conference, SADASC 2024, Tangier, Morocco, April 18–20, 2024, Proceedings, Part I
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This two-volume set CCIS 2167-2168 constitutes the proceedings of the 5th International Conference on Smart Applications and Data Analysis, SADASC 2024, held in Tangier, Morocco, in April 2024. The 30 full papers presented together with 10 short papers were carefully reviewed and selected from 91 submissions. They cover the following topics: designing and modeling; data management; tinyML and anomaly detection; network technologies and IOT; control, dynamic systems and optimisation; and exploitation and exploration.
5th International Conference, SADASC 2024, Tangier, Morocco, April 18–20, 2024, Proceedings, Part II
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This two-volume set CCIS 2167-2168 constitutes the proceedings of the 5th International Conference on Smart Applications and Data Analysis, SADASC 2024, held in Tangier, Morocco, in April 2024. The 30 full papers presented together with 10 short papers were carefully reviewed and selected from 91 submissions. They cover the following topics: designing and modeling; data management; tinyML and anomaly detection; network technologies and IOT; control, dynamic systems and optimisation; and exploitation and exploration.
Proceedings of EIDT, Ho Chi Minh City, Vietnam, November 12-14, 2025
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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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