AMAD
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| Datum: | 2001 |
| Titel: | Parallel Data Mining on a Beowulf Cluster |
| Mitwirkende: | The Pennsylvania State University CiteSeerX Archives |
| Autor*in: | Peter Strazdins Peter Christen Ole M. Nielsen Markus Hegland |
| Beschreibung: | This paper presents a parallel data mining application for predictive modelling running on a Beowulf style Linux cluster. Data mining or Knowledge Discovery in Databases (KDD) is the process of analysing large and complex data sets with the purpose of extracting useful and previously unknown knowledge. The task of predictive modelling is the prediction of an attribute according to a model built with one or more other attributes given in a data collection. We describe two methods for predictive modelling of high-dimensional data sets, namely ADDFIT which implements additive models, and HISURF which uses wavelets for high-dimensional surface smoothing, and present a parallel implementation on a distributed memory cluster architecture which uses the scripting language Python as a flexible front-end to facilitate user-interaction, control the parallel application, and generate graphical outputs. |
| URI: | https://www.amad.org/jspui/handle/123456789/73531 |
| Quelle: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.67.355 http://datamining.anu.edu.au/publications/2001/hpcasia2001.pdf.gz |
| AMAD ID: | 585301 |
| Enthalten in den Sammlungen: | BASE (Bielefeld Academic Search Engine) General history of Europe |
