Название: Advances in Data Clustering: Theory and Applications
Автор: Fadi Dornaika, Denis Hamad, Joseph Constantin, Vinh Truong Hoang
Издательство: Springer
Год: 2024
Страниц: 225
Язык: английский
Формат: pdf (true), epub
Размер: 26.8 MB

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 main challenge with unlabeled data is defining a quantifiable goal to guide the model-building process, which is the central theme of clustering. Unlike supervised learning, where the presence of labeled data provides a clear objective, unsupervised learning through clustering must derive its objectives from the inherent structure of the data itself. This requires sophisticated algorithms capable of discerning the underlying patterns and relationships within the data without prior knowledge or labels.

In summary, this book delves into the evolution and advancements in clustering techniques, emphasizing the transition from traditional shallow models to cuttingedge Deep Learning approaches. It explores the theoretical foundations, practical implementations, and diverse applications of clustering in modern data analytics. By leveraging the power of deep neural networks, we can significantly enhance clustering performance, thereby enabling more effective and insightful analysis of large and complex datasets.

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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