Temporal Data Mining via Unsupervised Ensemble Learning

Szerző: Yun Yang

Nyelv:

Kötés: Puha kötésű

Kiadó: Elsevier

Elérhetőség: Kiadói készleten rendelésre

Küldés 28-34 napon belül

19 436 Ft

Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal...

Információk a könyvről

Szerző

Yun Yang

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2016

oldal

172

EAN

9780128116548

ISBN

0128116544

Enbook ID

13717800

Kiadó

Elsevier

Súly

386

Méretek

192 x 234 x 9

Teljes leírás

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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