基于O型模糊关系的模糊聚类分析

本文首先证明了在O型模糊集的交、并运算分别是三角模和三角余模的前提之下，合理的有意义的交、并算子分别为∧和∨．在此基础上，我们讨论了利用O型模糊关系进行模糊聚类的意义及可行性。     

微机病毒与防范技术

本文阐述了计算机病毒的原理,综述了传统的计算机病毒防护方法,同时介绍了一些新的防护理论及方法.     

江苏田径竞技状况与发展对策

运用文献资料和数理统计等方法,对江苏历届全运田径竞技特征进行全面分析和研究,结果显示:在第10届全运会竞技中,江苏田径取得了历史性的突破,200m、20km竞走、4×100m、100m、铅球、4×400m、110m栏、跳远、马拉松、跳高和标枪为江苏省田径优势项目;200m、800m、1500m、3000m、10000m可能成为江苏新一轮全运会制胜项目。     

Self-Expanded Clustering Algorithm Based on Density Units with Evaluation Feedback Section

This paper presents an effective clustering mode and a novel clustering result evaluating mode. Clustering mode has two limited integral parameters. Evaluating mode evaluates clustering results and gives each a mark. The higher mark the clustering result gains, the higher quality it has. By organizing two modes in different ways, we can build two clustering algorithms： SECDU（Self-Expanded Clustering Algorithm based on Density Units） and SECDUF（Self-Expanded Clustering Algorithm Based on Density Units with Evaluation Feedback Section）. SECDU enumerates all value pairs of two parameters of clustering mode to process data set repeatedly and evaluates every clustering result by evaluating mode. Then SECDU output the clustering result that has the highest evaluating mark among all the ones. By applying ＂hill-climbing algorithm＂, SECDUF improves clustering efficiency greatly. Data sets that have different distribution features can be well adapted to both algorithms. SECDU and SECDUF can output high-quality clustering results. SECDUF tunes parameters of clustering mode automatically and no man＇s action involves through the whole process. In addition, SECDUF has a high clustering performance.     

A Chinese Web page clustering algorithm based on the suffix tree

In this paper, an improved algorithm, named STC-I. is proposed for Chinese Web page clustering based on Chinese language characteristics, which adopts a new unit choice principle and a novel suffix tree construction policy. The experimental results show that the new algorithm keeps advantages of STC, and is better than STC in precision and speed when they are used to cluster Chinese Web page.     

CFSBC： Clustering in High-Dimensional Space Based on Closed Frequent Item Set

Clustering in high-dimensional space is an important domain in data mining. It is the process of discovering groups in a high-dimensional dataset, in such way, that the similarity between the elements of the same cluster is maximum and between different clusters is minimal. Many clustering algorithms are not applicable to high dimensional space for its sparseness and decline properties. Dimensionality reduction is an effective method to solve this problem. The paper proposes a novel clustering algorithm CFSBC based onclosed frequent hemsets derived from association rule mining. which can get the clustering attributes with high efficiency. The algorithm has several advantages. First, it deals effectively with the problem of dimensionality reduction. Second, it is applicable to different kinds of attributes, Third, it is suitable for very large data sets. Experiment shows that the proposed algorithm is effective and efficient     

The refinement algorithm consideration in text clustering scheme based on multilevel graph

To construct a high efficient text clustering algorithm, the multilevel graph model and the refinement algorithm used in the uncoarsening phase is discussed, The model is applied to text clustering. The performance of clustering algorithm has to be improved with the refinement algorithm application, The experiment result demonstrated that the muhilevel graph text clustering algorithm is available,     

Neuromuscular synaptogenesis: clustering of acetylcholine receptors revisited

Clustering of neurotransmitter receptors in the postsynaptic membrane is critical for efficient synaptic transmission. During neuromuscular synaptogenesis, clustering of acetylcholine receptors (AChRs) is an early sign of postsynaptic differentiation. Recent studies have revealed that the earliest AChR clusters can form in the muscle independent of motorneurons. Neurally released agrin, acting through the muscle-specific kinase MuSK and rapsyn, then causes further clustering and localization of clusters underneath the nerve terminal. AChRs themselves are required for agrin-induced clustering of several postsynaptic proteins, most notably rapsyn. Once formed, AChR clusters are stabilized by several tyrosine kinases and by components of the dystrophin/utrophin glycoprotein complex, some of which also direct postnatal synaptic maturation such as formation of postjunctional folds. This review summarizes these recent results about AChR clustering, which indicate that early clustering can occur in the absence of nerves, that AChRs play an active role in the clustering process and that partly different mechanisms direct formation versus stabilization of AChR clusters. Received 10 April 2002; received after revision 4 June 2002; accepted 10 June 2002     

卫星移动通信系统星座设计仿真软件的研制

星座设计是卫星移动通信系统设计一个非常关键的内容,直接关系到系统的性能与成本,软件仿真是星座设计一个行之有效的方法,本文介绍了卫星移动通信系统星座设计仿真折设计方案,对软件的有关功能进行了介绍,该软件界面友好,具有丰富的地图操作内容,是军用及民用卫星移动通信系统星座设计仿真,演示的有效工具。     

基于自适应混合变异进化策略的神经模糊系统及应用研究

针对现存神经模糊系统中存在的问题,提出了基于自适应混合变异进化策略的神经模糊系统采用改进的最近邻域聚类算法对输入空间进行模糊聚类,确定模糊规则数以及模糊规则前件,这样做精简了模糊规则,不会因输入变量的增加而造成"维数灾难”;采用自适应混合进化策略确定模糊规则的后件,明显提高了算法的收敛速度和精度.将本文提出的基于自适应进化策略的神经模糊系统用于某炼油厂航煤干点的软测量建模,结果表明,该系统具有结构简单、建模精度高、泛化能力强等优点.