Advances in Spatial and Temporal Databases, Nikos Mamoulis; Thomas Seidl; Kristian Torp; Ira A

Автор: Pokorn?
Название: Advances in Databases and Information Systems
ISBN: 3319440381 ISBN-13(EAN): 9783319440385
Издательство: Springer
Рейтинг:
Цена: 8106.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This book constitutes the thoroughly refereed proceedings of the 20th East European Conference on Advances in Databases and Information Systems, ADBIS 2016, held in Prague, Czech Republic, in August 2016.The 21 full papers presented together with two keynote papers and one keynote abstract were carefully selected and reviewed from 85 submissions. The papers are organized in topical sections such as data quality, mining, analysis and clustering; model-driven engineering, conceptual modeling; data warehouse and multidimensional modeling, recommender systems; spatial and temporal data processing; distributed and parallel data processing; internet of things and sensor networks.

Автор: Ramamohanarao Kotagiri; P. Radha Krishna; Mukesh M
Название: Advances in Databases: Concepts, Systems and Applications
ISBN: 3540717021 ISBN-13(EAN): 9783540717027
Издательство: Springer
Рейтинг:
Цена: 22359.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Constitutes the refereed proceedings of the 12th International Conference on Database Systems for Advanced Applications, DASFAA 2007, held in Bangkok, Thailand, April 2007. This book covers query language and query optimization, data mining and knowledge discovery, P2P and grid-based data management, and XML databases.

Автор: Yang Yun
Название: Temporal Data Mining via Unsupervised Ensemble Learning
ISBN: 0128116544 ISBN-13(EAN): 9780128116548
Издательство: Elsevier Science
Рейтинг:
Цена: 7241.00 р.
Наличие на складе: Поставка под заказ.

Описание: 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.