Temporal Data Mining Via Unsupervised Ensemble Learning

"Temporal Data Mining via Unsupervised Ensemble Learning" provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning, and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book is further shaped with a practical focus of fundamental knowledge and techniques, and contains a rich blend of theory and practice. Furthermore, this book provides illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodology and guide to proper usage of all methods. There is nothing universal that can solve all problems and it is important to understand the characteristics of both clustering algorithms and the target temporal data, so that the right approach can be selected for a given clustering problem. Scientists, researchers, and data analysts working with machine learning and data mining will benefit from this innovative book as well as will undergraduate and graduate students following courses in computer science, engineering and statistics. Includes fundamental concepts and knowledge, covering all key tasks and techniques of temporal data mining i.e., temporal data representations, similarity measure, and mining tasksConcentrates on temporal data clustering tasks from different perspectives, including major algorithms from clustering algorithms and ensemble learning approachesPresents a rich blend of theory and practice, addressing seminal research ideas and also looking at the technology from a practical point of view

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  • Author : Yun Yang
  • Publisher : Elsevier
  • Pages : 196 pages
  • ISBN : 9780128116548
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKTemporal Data Mining Via Unsupervised Ensemble Learning

Temporal Data Mining Via Unsupervised Ensemble Learning

Temporal Data Mining Via Unsupervised Ensemble Learning
  • Author : Yun Yang
  • Publisher : Elsevier
  • Release : 15 December 2016
GET THIS BOOKTemporal Data Mining Via Unsupervised Ensemble Learning

"Temporal Data Mining via Unsupervised Ensemble Learning" provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning, and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book is further shaped with a practical focus of fundamental knowledge and techniques, and contains a rich blend of theory and practice. Furthermore, this book provides illustrations of the proposed approaches based on data and simulation experiments

Temporal Data Mining via Unsupervised Ensemble Learning

Temporal Data Mining via Unsupervised Ensemble Learning
  • Author : Yun Yang
  • Publisher : Elsevier
  • Release : 15 November 2016
GET THIS BOOKTemporal Data Mining via Unsupervised Ensemble Learning

Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice. Furthermore, the book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all

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  • Publisher : Springer
  • Release : 20 May 2018
GET THIS BOOKComputational Intelligence and Its Applications

This book constitutes the refereed proceedings of the 6th IFIP TC 5 International Conference on Computational Intelligence and Its Applications, CIIA 2018, held in Oran, Algeria, in May 2018. The 56 full papers presented were carefully reviewed and selected from 202 submissions. They are organized in the following topical sections: data mining and information retrieval; evolutionary computation; machine learning; optimization; planning and scheduling; wireless communication and mobile computing; Internet of Things (IoT) and decision support systems; pattern recognition and image processing; and semantic web services.

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  • Publisher : Morgan Kaufmann
  • Release : 10 March 2019
GET THIS BOOKMeta-Analytics

Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book virtually ensures that at least one pattern will lead to better overall system behavior than the use of traditional analytics approaches. The book is ‘meta’ to analytics, covering general analytics in sufficient detail for readers to engage with, and understand, hybrid or meta- approaches. The book has relevance to machine

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  • Publisher : Springer Nature
  • Release : 06 December 2019
GET THIS BOOKTelematics and Computing

This book constitutes the thoroughly refereed proceedings of the 8th International Congress on Telematics and Computing, WITCOM 2019, held in Merida, Mexico, in November 2019. The 31 full papers presented in this volume were carefully reviewed and selected from 78 submissions. The papers are organized in topical sections: ​GIS & climate change; telematics & electronics; artificial intelligence & machine learning; software engineering & education; internet of things; and informatics security.

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  • Author : Jiawei Han,Jian Pei,Micheline Kamber
  • Publisher : Elsevier
  • Release : 09 June 2011
GET THIS BOOKData Mining: Concepts and Techniques

Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing

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  • Publisher : Springer Nature
  • Release : 22 September 2021
GET THIS BOOKICT Analysis and Applications

This book proposes new technologies and discusses future solutions for ICT design infrastructures, as reflected in high-quality papers presented at the 5th International Conference on ICT for Sustainable Development (ICT4SD 2020), held in Goa, India, on 23-24 July 2020. The conference provided a valuable forum for cutting-edge research discussions among pioneering researchers, scientists, industrial engineers, and students from all around the world. Bringing together experts from different countries, the book explores a range of central issues from an international perspective.

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  • Author : Michael W. Berry,Azlinah Mohamed,Bee Wah Yap
  • Publisher : Springer Nature
  • Release : 04 September 2019
GET THIS BOOKSupervised and Unsupervised Learning for Data Science

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students

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  • Publisher : Horwood Publishing
  • Release : 14 May 2007
GET THIS BOOKMachine Learning and Data Mining

Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. Written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining, this text is suitable foradvanced undergraduates, postgraduates and tutors in a wide area of computer science and technology, as well as

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  • Author : Theophano Mitsa
  • Publisher : CRC Press
  • Release : 10 March 2010
GET THIS BOOKTemporal Data Mining

Temporal data mining deals with the harvesting of useful information from temporal data. New initiatives in health care and business organizations have increased the importance of temporal information in data today. From basic data mining concepts to state-of-the-art advances, Temporal Data Mining covers the theory of this subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and

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  • Author : Wang, John
  • Publisher : IGI Global
  • Release : 31 August 2008
GET THIS BOOKEncyclopedia of Data Warehousing and Mining, Second Edition

There are more than one billion documents on the Web, with the count continually rising at a pace of over one million new documents per day. As information increases, the motivation and interest in data warehousing and mining research and practice remains high in organizational interest. The Encyclopedia of Data Warehousing and Mining, Second Edition, offers thorough exposure to the issues of importance in the rapidly changing field of data warehousing and mining. This essential reference source informs decision makers,

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  • Publisher : Academic Press
  • Release : 24 April 2020
GET THIS BOOKNature-Inspired Computation and Swarm Intelligence

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  • Publisher : MIT Press
  • Release : 24 August 2012
GET THIS BOOKMachine Learning

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary

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  • Publisher : Academic Press
  • Release : 10 September 2014
GET THIS BOOKQuantum Machine Learning

Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary