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This book highlights the state of the art and recent advances in
Big Data clustering methods and their innovative applications in
contemporary AI-driven systems. The book chapters discuss Deep
Learning for Clustering, Blockchain data clustering, Cybersecurity
applications such as insider threat detection, scalable distributed
clustering methods for massive volumes of data; clustering Big Data
Streams such as streams generated by the confluence of Internet of
Things, digital and mobile health, human-robot interaction, and
social networks; Spark-based Big Data clustering using Particle
Swarm Optimization; and Tensor-based clustering for Web graphs,
sensor streams, and social networks. The chapters in the book
include a balanced coverage of big data clustering theory, methods,
tools, frameworks, applications, representation, visualization, and
clustering validation.
This book presents advances in business computing and data
analytics by discussing recent and innovative machine learning
methods that have been designed to support decision-making
processes. These methods form the theoretical foundations of
intelligent management systems, which allows for companies to
understand the market environment, to improve the analysis of
customer needs, to propose creative personalization of contents,
and to design more effective business strategies, products, and
services. This book gives an overview of recent methods - such as
blockchain, big data, artificial intelligence, and cloud computing
- so readers can rapidly explore them and their applications to
solve common business challenges. The book aims to empower readers
to leverage and develop creative supervised and unsupervised
methods to solve business decision-making problems.
This book highlights the state of the art and recent advances in
Big Data clustering methods and their innovative applications in
contemporary AI-driven systems. The book chapters discuss Deep
Learning for Clustering, Blockchain data clustering, Cybersecurity
applications such as insider threat detection, scalable distributed
clustering methods for massive volumes of data; clustering Big Data
Streams such as streams generated by the confluence of Internet of
Things, digital and mobile health, human-robot interaction, and
social networks; Spark-based Big Data clustering using Particle
Swarm Optimization; and Tensor-based clustering for Web graphs,
sensor streams, and social networks. The chapters in the book
include a balanced coverage of big data clustering theory, methods,
tools, frameworks, applications, representation, visualization, and
clustering validation.
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