Parallel Data Mining Algorithms (Mathematics Research Developments) - Hardcover

Xiaochun Wang

Hardcover

ISBN 13: 9798895309902

Publisher: Nova Science Publishers, Inc., 2026

Synopsis

Many modern data mining problems cannot be solved efficiently by the traditional algorithms. Obtaining optimal solutions in a tolerable amount of time, parallel data mining algorithms can be chosen as a suitable tool to solve the aforesaid problems. However, most up-to-date books on parallel and distributed computing techniques, such as Hadoop and Spark, are not wholly on the data mining subjects, covering only a portion of the materials in one or a few chapters and touching on the subjects but without going into it deeply, and thus incomplete and lacking systematicity, specialty and comprehensiveness. This book uniquely combines systematicity and comprehensiveness, trying not only to present as many data mining algorithms developed throughout till now as possible but also to provide their parallel solutions developed by the research and industry community currently. It covers a broad range of data mining algorithms in depth, provides a comprehensive coverage of the state of the arts and advances in parallel data mining algorithm research (covering such topics as the parallel algorithm design in general and the implementations by the divide-and-conquer scheme, the MapReduce programming model and the Resilient Distributed Dataset (RDD) in specific) and makes their design and analysis accessible to all levels of readers with self-contained chapters and algorithms in pseudocode and with updated notes and bibliography to reflect developments in the field.

Edition details

• Publisher: Nova Science Publishers, Inc.
• Publication date: 2026
• Language: English
• ISBN 13: 9798895309902
• Binding: Hardcover
• Number of pages: 288