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...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Elsevier Science
2016
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| Schlagworte: | |
| Online-Zugang: | FLA01 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | 1 online resource |
| ISBN: | 0128118415 9780128118412 |
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| author | Yang, Yun |
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| discipline | Informatik |
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| id | DE-604.BV046127035 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:35:55Z |
| institution | BVB |
| isbn | 0128118415 9780128118412 |
| language | English |
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| publisher | Elsevier Science |
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| spelling | Yang, Yun Verfasser aut Temporal Data Mining via Unsupervised Ensemble Learning Elsevier Science 2016 1 online resource txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references and index 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 COMPUTERS / General bisacsh Data mining fast Machine learning fast Temporal databases fast Data mining Temporal databases Machine learning Erscheint auch als Druck-Ausgabe 0128116544 Erscheint auch als Druck-Ausgabe 9780128116548 http://www.sciencedirect.com/science/book/9780128116548 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Yang, Yun Temporal Data Mining via Unsupervised Ensemble Learning COMPUTERS / General bisacsh Data mining fast Machine learning fast Temporal databases fast Data mining Temporal databases Machine learning |
| title | Temporal Data Mining via Unsupervised Ensemble Learning |
| title_auth | Temporal Data Mining via Unsupervised Ensemble Learning |
| title_exact_search | Temporal Data Mining via Unsupervised Ensemble Learning |
| title_full | Temporal Data Mining via Unsupervised Ensemble Learning |
| title_fullStr | Temporal Data Mining via Unsupervised Ensemble Learning |
| title_full_unstemmed | Temporal Data Mining via Unsupervised Ensemble Learning |
| title_short | Temporal Data Mining via Unsupervised Ensemble Learning |
| title_sort | temporal data mining via unsupervised ensemble learning |
| topic | COMPUTERS / General bisacsh Data mining fast Machine learning fast Temporal databases fast Data mining Temporal databases Machine learning |
| topic_facet | COMPUTERS / General Data mining Machine learning Temporal databases |
| url | http://www.sciencedirect.com/science/book/9780128116548 |
| work_keys_str_mv | AT yangyun temporaldataminingviaunsupervisedensemblelearning |