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。     

澜沧—耿马大震前后尾波Q值的研究

本文根据Aki的尾波散射理论,对澜沧-耿马大震前后近10年的时间,在大震区域及外围的7个地震台近2000多张地震记录图的尾波进行测量,得出Q值随时间变化的结果表明:澜沧、耿马两次大震前有1～3年左右的低Q值异常期;在临近大震前1年左右,Q值处于回升阶段;震后Q值明显增大.并结合澜沧-耿马地区区域地质构造图,试图分析Q值在断层错动中随时间变化的特征;澜沧、耿马7.6、7.2级两次大震是分别孕育的双主震并且可能是澜沧7.6级大震的发生触发了耿马7.2级大震的发生。     

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     

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.     

一种新的混合聚类算法

聚类是数据挖掘的主要技术之一,是一种无导师监督的模式识别方式。聚类分析就是按照数据间的相似程度,依据特定的准则将数据划分成不同子类。K-平均算法是经典的聚类算法。蚂蚁聚类算法是近来涌现的新的聚类算法,它通过模拟蚁群的智能行为进行聚类分析,已经在数据挖掘中得到应用。通过分析蚂蚁聚类算法和K-平均算法两种不同聚类算法的基本思想,将两种算法结合得到混合聚类算法,仿真实验证明混合聚类算法的算法性能优于蚂蚁算法和K-平均算法。     

基于最邻近聚类和向量模糊c-均值的混沌预测

针对混沌时间序列难预测的问题,提出一种新的基于最邻近聚类和向量模糊c-均值(FCMV)聚类算法的模糊建模方法。其前提参数辨识分两步,首先用最近邻聚类法初始划分输入空间,得到规则数及初始聚类中心,再用FCMV把具有相同收敛向量的聚类中心归到同一个区域来优化前一步得到的聚类中心,得到前提参数;采用递推最小二乘算法辨识模型的结论参数。最后通过对Mackey-Glass混沌时间序列的建模和预测验证了该方法的有效性与实用性。