Name
Abstract | ||
|---|---|---|
This paper introduces a distributed data mining approach suited to grid computing environments based on a supervised learning classifier system. Specific Classifier and Majority Voting methods for Distributed Data Mining (DDM) are explored and compared with the Centralized Data Mining (CDM) approach. Experimental tests were conducted considering a real world data set from the intensive care medicine in order to predict the outcome of the patients. The results demonstrate that the performance of the DDM methods are better than the CDM method. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-24352-3_26 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Intensive Care Medicine,Outcome Prediction,Grid Data Mining,Distributed Data Mining,Centralized Data Mining | Data mining,Grid computing,Computer science,Intensive care medicine,Supervised learning,Majority rule,Classifier (linguistics),Grid | Conference |
Volume | ISSN | Citations |
221 | 1865-0929 | 2 |
PageRank | References | Authors |
0.40 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manuel Filipe Santos | 1 | 360 | 68.91 |
| Wesley Mathew | 2 | 104 | 4.13 |
| Carlos Filipe Portela | 3 | 5 | 2.21 |