Buch, Englisch, 172 Seiten, Format (B × H): 192 mm x 234 mm, Gewicht: 386 g
Buch, Englisch, 172 Seiten, Format (B × H): 192 mm x 234 mm, Gewicht: 386 g
ISBN: 978-0-12-811654-8
Verlag: Elsevier Science Publishing Co Inc
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.
Zielgruppe
<p>Undergraduate and graduate students who major in machine learning and data mining. Scientists, researchers and data analysts working on temporal data mining, ensemble learning.</p>
Fachgebiete
Weitere Infos & Material
1. Introduction2. Temporal Data Mining3. Temporal Data Clustering4. Ensemble Learning5. HMM-Based Hybrid Meta-Clustering in Association With Ensemble Technique6. Unsupervised Learning via an Iteratively Constructed Clustering Ensemble7. Temporal Data Clustering via a Weighted Clustering Ensemble With Different Representations8. Conclusions, Future Work
