Scalable High Performance Computing for Knowledge Discovery and Data Mining
Paul Stolorz (Editor), J. Pinho de SousaBooks.org participates in affiliate programs including Bookshop.org and the Amazon Services LLC Associates Program. We may earn a commission from qualifying purchases made through links on this page, at no additional cost to you.
Overview
Scalable High Performance Computing for Knowledge Discovery and Data Mining brings together in one place important contributions and up-to-date research results in this fast moving area. Scalable High Performance Computing for Knowledge Discovery and Data Mining serves as an excellent reference, providing insight into some of the most challenging research issues in the field.Synopsis
Scalable High Performance Computing for Knowledge Discovery and Data Mining brings together in one place important contributions and up-to-date research results in this fast moving area.
Scalable High Performance Computing for Knowledge Discovery and Data Mining serves as an excellent reference, providing insight into some of the most challenging research issues in the field.
Booknews
This special issue of , v.1, no.4 (1997) features four papers on sophisticated means to cope with information overload, with an editorial by Stolorz (Jet Propulsion Laboratory) and Musick (Lawrence Livermore National Laboratory) on the new interdisciplinary field of Knowledge Discovery in Databases. The focus is on applications of KDD methods on scalable high-performance computing platforms. Each contribution treats a KDD problem involving massive datasets, describes analytic techniques (e.g. association rules, On-Line Analytical Processing) grounded in a particular discipline (such as astrophysical -body simulations), and tests parallel versions of these methods. There appears to be quite a future for meta-supercomputing. No index. Annotation c. by Book News, Inc., Portland, Or.