Adaptive Resonance Theory In Social Media Data Clustering
Download Adaptive Resonance Theory In Social Media Data Clustering PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Adaptive Resonance Theory In Social Media Data Clustering book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Adaptive Resonance Theory in Social Media Data Clustering
Social media data contains our communication and online sharing, mirroring our daily life. This book looks at how we can use and what we can discover from such big data: Basic knowledge (data & challenges) on social media analytics Clustering as a fundamental technique for unsupervised knowledge discovery and data mining A class of neural inspired algorithms, based on adaptive resonance theory (ART), tackling challenges in big social media data clustering Step-by-step practices of developing unsupervised machine learning algorithms for real-world applications in social media domain Adaptive Resonance Theory in Social Media Data Clustering stands on the fundamental breakthrough in cognitive and neural theory, i.e. adaptive resonance theory, which simulates how a brain processes information to perform memory, learning, recognition, and prediction. It presents initiatives on the mathematical demonstration of ART’s learning mechanisms in clustering, and illustrates how to extend the base ART model to handle the complexity and characteristics of social media data and perform associative analytical tasks. Both cutting-edge research and real-world practices on machine learning and social media analytics are included in the book and if you wish to learn the answers to the following questions, this book is for you: How to process big streams of multimedia data? How to analyze social networks with heterogeneous data? How to understand a user’s interests by learning from online posts and behaviors? How to create a personalized search engine by automatically indexing and searching multimodal information resources? .
Advanced Data Mining and Applications
This book constitutes the refereed proceedings of the 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, held in Shenyang, China, during August 21–23, 2023. The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.
Cities and the Digital Revolution
This book explores the emergence and development of data in cities. It exposes how Information Communication Technology (ICT) corporations seeking to capitalize on cities developing needs for urban technologies have contributed to many of the issues we are faced with today, including urbanization, centralization of wealth and climate change. Using several case studies, the book provides examples of the, in part, detrimental effects ICT driven ‘Smart City’ solutions have had and will have on the human characteristics that contribute to the identity and sense of belonging innate to many of our cities. The rise in Artificial Intelligence, Big Data, and technologies like social media, has changed how people interact with and in cities, and Allam discusses of how these changes require planners, engineers and other urban professionals to adjust their approach. The main question the book seeks to address is ‘how can we use emerging technologies to recalibrate our cities and ensure increased livability, whilst also effectively dealing with their associate challenges?’ This is an ongoing conversation, but one that requires extensive thought as it has extensive consequences. This book will be of interest to students, academics, professionals and policy makers across a broad range of subjects including urban studies, architecture and STS, geography and social policy.