Classification is one of the most widely used method in data mining with numerous applications in biomedicine. The scope and the resolution of data involved in many real life applications require very efficient implementations of classification methods, developed to run on parallel or distributed computational systems. In this study, a parallel implementation of an efficient algorithm that is based on regularized general eigenvalue classification is introduced. The proposed implementation is tested on a very large scale genomic data base and preliminary results regarding efficiency are presented.
A Parallel Classification Method for Genomic and Proteomic Problems
Guarracino Mario Rosario;
2006
Abstract
Classification is one of the most widely used method in data mining with numerous applications in biomedicine. The scope and the resolution of data involved in many real life applications require very efficient implementations of classification methods, developed to run on parallel or distributed computational systems. In this study, a parallel implementation of an efficient algorithm that is based on regularized general eigenvalue classification is introduced. The proposed implementation is tested on a very large scale genomic data base and preliminary results regarding efficiency are presented.File in questo prodotto:
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