The motivation of data mining is how to extract effective information from huge data in very large database. However, some redundant and irrelevant attributes, which result in low performance and high computing comple...The motivation of data mining is how to extract effective information from huge data in very large database. However, some redundant and irrelevant attributes, which result in low performance and high computing complexity, are included in the very large database in general.So, Feature Subset Selection (FSS) becomes one important issue in the field of data mining. In this letter, an FSS model based on the filter approach is built, which uses the simulated annealing genetic algorithm. Experimental results show that convergence and stability of this algorithm are adequately achieved.展开更多
In this paper we give a parallel algorithm for obtaining the eigenvalues and eigenvectors of a matrix.The practical background of this algorithm is the numerical computation in conjunction with the symbolic computation.
基金Supported by the Project of the Science and Technology Plan of Chongqing City
文摘The motivation of data mining is how to extract effective information from huge data in very large database. However, some redundant and irrelevant attributes, which result in low performance and high computing complexity, are included in the very large database in general.So, Feature Subset Selection (FSS) becomes one important issue in the field of data mining. In this letter, an FSS model based on the filter approach is built, which uses the simulated annealing genetic algorithm. Experimental results show that convergence and stability of this algorithm are adequately achieved.
文摘In this paper we give a parallel algorithm for obtaining the eigenvalues and eigenvectors of a matrix.The practical background of this algorithm is the numerical computation in conjunction with the symbolic computation.
