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Opis

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

Szczegóły książki

Kategoria Książki po angielsku Computing & information technology Computer programming / software development Database programming

• Pełny tytuł: Temporal Data Mining via Unsupervised Ensemble Learning
• Autor: Yun Yang
• Język: Angielski
• Oprawa: Miękka
• Liczba stron: 172
• EAN: 9780128116548
• ISBN: 0128116544
• ID: 13717800
• Wydawca: Elsevier
• Waga: 386 g
• Wymiary: 234 × 192 × 9 mm
• Data wydania: 18. November 2016

Ulubione w innej kategorii

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