Tag: Clustering

Advances in Data Clustering Theory and Applications


Free Download Advances in Data Clustering: Theory and Applications
English | 2024 | ISBN: 9819776783 | 231 Pages | PDF EPUB (True) | 22 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.

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Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks


Free Download Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks by Patricia Melin , Martha Ramirez , Oscar Castillo
English | PDF EPUB (True) | 2024 | 82 Pages | ISBN : 3031711009 | 9.5 MB
This book provides a new model for clustering, classification, and time series prediction by using artificial neural networks to computationally simulate the behavior of the cognitive functions of the brain is presented. This model focuses on the study of intelligent hybrid neural systems and their use in time series analysis and decision support systems. Therefore, through the development of eight case studies, multiple time series related to the following problems are analyzed: traffic accidents, air quality and multiple global indicators (energy consumption, birth rate, mortality rate, population growth, inflation, unemployment, sustainable development, and quality of life). The main contribution consists of a Generalized Type-2 fuzzy integration of multiple indicators (time series) using both supervised and unsupervised neural networks and a set of Type-1, Interval Type-2, and Generalized Type-2 fuzzy systems. The obtained results show the advantages of the proposed model of Generalized Type-2 fuzzy integration of multiple time series attributes. This book is intended to be a reference for scientists and engineers interested in applying type-2 fuzzy logic techniques for solving problems in classification and prediction. We consider that this book can also be used to get novel ideas for new lines of research, or to continue the lines of research proposed by the authors of the book.

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Akka Remoting and Clustering with Scala Rock the JVM

Last updated 4/2022
Created by Daniel Ciocîrlan
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 20 Lectures ( 6h 50m ) | Size: 4.23 GB
For Scala and Akka developers: write reactive distributed systems with remote actors, Akka Clustering and Scala
Free Download What you’ll learn:

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Clustering Challenges in Biological Networks


Free Download Clustering Challenges in Biological Networks by W Art Chaovalitwongse, Sergiy Butenko, Panos M Pardalos
English | 2009 | ISBN: 9812771654 | 334 Pages | PDF | 22.3 MB
This volume presents a collection of papers dealing with various aspects of clustering in biological networks and other related problems in computational biology.

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Feature and Dimensionality Reduction for Clustering with Deep Learning


Free Download Feature and Dimensionality Reduction for Clustering with Deep Learning
English | 2024 | ISBN: 3031487427 | 460 Pages | PDF EPUB (True) | 34 MB
This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by "family" to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.

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