Temporal Data Mining via Unsupervised Ensemble Learning Books

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Temporal Data Mining Via Unsupervised Ensemble Learning


Temporal Data Mining Via Unsupervised Ensemble Learning
  • Author : Yun Yang
  • Publisher : Elsevier
  • Release : 2016-12-15
  • ISBN : 0128116544
  • Language : En, Es, Fr & De
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"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

Temporal Data Mining via Unsupervised Ensemble Learning


Temporal Data Mining via Unsupervised Ensemble Learning
  • Author : Yun Yang
  • Publisher : Elsevier
  • Release : 2016-11-15
  • ISBN : 9780128118412
  • Language : En, Es, Fr & De
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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 methodologies, and is a guide to the proper usage of these methods. As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct 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 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 tasks Concentrates on temporal data clustering tasks from different perspectives, including major algorithms from clustering algorithms and ensemble learning approaches Presents a rich blend of theory and practice, addressing seminal research ideas and looking at the technology from a practical point-of-view

Meta Analytics


Meta Analytics
  • Author : Steven Simske
  • Publisher : Morgan Kaufmann
  • Release : 2019-03-10
  • ISBN : 9780128146248
  • Language : En, Es, Fr & De
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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 translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance. Inn addition, the analytics within can be applied to predictive algorithms for everyone from police departments to sports analysts. Provides comprehensive and systematic coverage of machine learning-based data analysis tasks Enables rapid progress towards competency in data analysis techniques Gives exhaustive and widely applicable patterns for use by data scientists Covers hybrid or ‘meta’ approaches, along with general analytics Lays out information and practical guidance on data analysis for practitioners working across all sectors

Video Mining


Video Mining
  • Author : Azriel Rosenfeld
  • Publisher : Springer Science & Business Media
  • Release : 2013-03-09
  • ISBN : 9781475769289
  • Language : En, Es, Fr & De
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Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding. Recently, computer vision has gradually been making the transition away from understanding single images to analyzing image sequences, or video Video understanding deals with understanding of video understanding. sequences, e.g., recognition of gestures, activities, facial expressions, etc. The main shift in the classic paradigm has been from the recognition of static objects in the scene to motion-based recognition of actions and events. Video understanding has overlapping research problems with other fields, therefore blurring the fixed boundaries. Computer graphics, image processing, and video databases have obvi ous overlap with computer vision. The main goal of computer graphics is to generate and animate realistic looking images, and videos. Re searchers in computer graphics are increasingly employing techniques from computer vision to generate the synthetic imagery. A good exam pIe of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is derived from real images using computer vision techniques. Here the shift is from synthesis to analy sis followed by synthesis. Image processing has always overlapped with computer vision because they both inherently work directly with images.

Encyclopedia of Data Warehousing and Mining Second Edition


Encyclopedia of Data Warehousing and Mining  Second Edition
  • Author : Wang, John
  • Publisher : IGI Global
  • Release : 2008-08-31
  • ISBN : 9781605660110
  • Language : En, Es, Fr & De
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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, problem solvers, and data mining specialists in business, academia, government, and other settings with over 300 entries on theories, methodologies, functionalities, and applications.