The performance of the classical clustering algorithm is not always satisfied with the high-dimensional datasets, which make clustering method limited in many application. To solve this problem, clustering method with...The performance of the classical clustering algorithm is not always satisfied with the high-dimensional datasets, which make clustering method limited in many application. To solve this problem, clustering method with Projection Pursuit dimension reduction based on Immune Clonal Selection Algorithm (ICSA-PP) is proposed in this paper. Projection pursuit strategy can maintain consistent Euclidean distances between points in the low-dimensional embeddings where the ICSA is used to search optimizing projection direction. The proposed algorithm can converge quickly with less iteration to reduce dimension of some high-dimensional datasets, and in which space, K-mean clustering algorithm is used to partition the reduced data. The experiment results on UCI data show that the presented method can search quicker to optimize projection direction than Genetic Algorithm (GA) and it has better clustering results compared with traditional linear dimension reduction method for Principle Component Analysis (PCA).展开更多
The Faddeev AGS equations for the coupled-channels ■NN-πΣN system with quantum numbers I=1/2 and S=0 are solved. Using separable potentials for the ■N-πΣ interaction, we calculate the transition probability for ...The Faddeev AGS equations for the coupled-channels ■NN-πΣN system with quantum numbers I=1/2 and S=0 are solved. Using separable potentials for the ■N-πΣ interaction, we calculate the transition probability for the(YK)I=0 + N→πΣN reaction. The possibility to observe the trace of the K-pp quasi-bound state in πΣN mass spectra was studied. Various types of chiral-based and phenomenological potentials are used to describe the ■N-πΣ interaction. Finally, we show that we can observe the signature of the K-pp quasi-bound state in the mass spectra, as well as the trace of branch points in the observables.展开更多
基金Supported by the National Natural Science Foundation of China (No. 61003198, 60703108, 60703109, 60702062,60803098)the National High Technology Development 863 Program of China (No. 2008AA01Z125, 2009AA12Z210)+1 种基金the China Postdoctoral Science Foundation funded project (No. 20090460093)the Provincial Natural Science Foundation of Shaanxi, China (No. 2009JQ8016)
文摘The performance of the classical clustering algorithm is not always satisfied with the high-dimensional datasets, which make clustering method limited in many application. To solve this problem, clustering method with Projection Pursuit dimension reduction based on Immune Clonal Selection Algorithm (ICSA-PP) is proposed in this paper. Projection pursuit strategy can maintain consistent Euclidean distances between points in the low-dimensional embeddings where the ICSA is used to search optimizing projection direction. The proposed algorithm can converge quickly with less iteration to reduce dimension of some high-dimensional datasets, and in which space, K-mean clustering algorithm is used to partition the reduced data. The experiment results on UCI data show that the presented method can search quicker to optimize projection direction than Genetic Algorithm (GA) and it has better clustering results compared with traditional linear dimension reduction method for Principle Component Analysis (PCA).
文摘The Faddeev AGS equations for the coupled-channels ■NN-πΣN system with quantum numbers I=1/2 and S=0 are solved. Using separable potentials for the ■N-πΣ interaction, we calculate the transition probability for the(YK)I=0 + N→πΣN reaction. The possibility to observe the trace of the K-pp quasi-bound state in πΣN mass spectra was studied. Various types of chiral-based and phenomenological potentials are used to describe the ■N-πΣ interaction. Finally, we show that we can observe the signature of the K-pp quasi-bound state in the mass spectra, as well as the trace of branch points in the observables.
