Springer Randomized Algorithms in Automatic Control and Data Mining: 67 (Intelligent Systems Reference Library, 67)

Product Description 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. Review “The book is a good resource for scientists, researchers, and practitioners who have a basic understanding of algorithms, control, and data mining in intelligent systems. … This book is for people from disciplines such as computer science, engineering, and mathematics, who plan to work on randomized algorithms in intelligent systems. It is written in a scientific way, hence it can also be used as a graduate-level textbook.” (Gulustan Dogan, Computing Reviews, August 21, 2019) “This book carefully and broadly covers aspects related to data, data mining, information, and knowledge. The reader will find it very interesting to go through the evolutionary path that the field of data mining has traversed. Each area of data mining is clearly defined in this book, supported by algorithms and small examples. … the book will generate interest in researchers involved in data structures and algorithms.” (Harekrishna Misra, Computing Reviews, April, 2015) From the Back Cover 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.