In the big data era, many modern data mining problems cannot be solved efficiently by the traditional algorithms, (i.e., specifically intractable by a single-processor computing system, either spatially prohibitive, temporally prohibitive or both). 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. Parallel Data Mining Algorithms 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. Instead of scratching the surface, 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, in the hope that it will become an introduction-level parallel data mining algorithm textbook in universities as well as the standard reference for professionals.
