Randomized Algorithms in Automatic Control and Data Mining

Oleg Granichin

ISBN 10: 3662522918 / ISBN 13: 9783662522912

Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg Sep 2016, 2016

About this Item

Neuware -In the fields of data mining and control, the huge amount of unstructured data and the presence of uncertainty in system descriptions have always been critical issues. The book Randomized Algorithms in Automatic Control and Data Mining introduces the readers to the fundamentals of randomized algorithm applications in data mining (especially clustering) and in automatic control synthesis. The methods proposed in this book guarantee that the computational complexity of classical algorithms and the conservativeness of standard robust control techniques will be reduced. It is shown that when a problem requires 'brute force' in selecting among options, algorithms based on random selection of alternatives offer good results with certain probability for a restricted time and significantly reduce the volume of operations.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 276 pp. Englisch.

Seller Inventory # 9783662522912

Bibliographic Details

• Title: Randomized Algorithms in Automatic Control and Data Mining
• Publisher: Springer Berlin Heidelberg, Springer Berlin Heidelberg Sep 2016
• Publication Date: 2016
• Language: English
• ISBN 10: 3662522918
• ISBN 13: 9783662522912
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

In the fields of data mining and control, the huge amount of unstructured data and the presence of uncertainty in system descriptions have always been critical issues. The book Randomized Algorithms in Automatic Control and Data Mining introduces the readers to the fundamentals of randomized algorithm applications in data mining (especially clustering) and in automatic control synthesis. The methods proposed in this book guarantee that the computational complexity of classical algorithms and the conservativeness of standard robust control techniques will be reduced. It is shown that when a problem requires "brute force" in selecting among options, algorithms based on random selection of alternatives offer good results with certain probability for a restricted time and significantly reduce the volume of operations.