基于数据挖掘技术的青少年健康心理状况研究

目前海量的数据存储已远超过了人们的理解能力，尤其是现代医学数据量越来越大，而传统的统计技术及数据管理工具已经不能满足需求。知识发现及数据挖掘工具在处理海量数据库时显示了它们的长处。介绍了数据挖掘的概念和方法及其在青少年健康心理状况研究中的应用。     

Density-based clustering method in the moving object database

With the rapid advance of wireless communication, tracking the positions of the moving objects is becoming increasingly feasible and necessary. Because a large number of people use mobile phones, we must handle a large moving object database as well as the following problems. How can we provide the customers with high quality service, that means, how can we deal with so many enquiries within as less time as possible? Because of the large number of data, the gap between CPU speed and the size of main memory has increasing considerably. One way to reduce the time to handle enquiries is to reduce the I/O number between the buffer and the secondary storage. An effective clustering of the objects can minimize the I/O-cost between them. In this paper, according to the characteristic of the moving object database, we analyze the objects in buffer, according to their mappings in the two-dimension coordinate, and then develop a density-based clustering method to effectively reorganize the clusters. This new mechanism leads to the less cost of the I/O operation and the more efficient response to enquiries.     

基于连通域的版面分割研究

版面分割是OCR（光学字符识别）系统的重要组成部分，在将印刷文档转换为电子文档的过程中是必不可少的，该文对基于连通域的版面分割方法进行研究，首先用动态聚类法选取模糊阈值，然后对文档图像进行模糊处理，形成大的连通区域，再根据连通域的位置关系等分割出文档的各个分栏，实验表明，该方法对于简单的矩形版面及复杂的版面，例如，非Manhattan版面等都能较好地分割。     

电流变机理研究综述Ⅲ─—材料研究的成就、问题与前景

本文全面介绍了电流变材料的发展，包括电流变液、磁流变液和电磁流变液；此外还详细讨论了固相化学性质和添加剂与电流交响应的关系；指出了电流变材料的研究方向。作为这篇评论的结语概括介绍了电流变应用研究的最新成就。     

某些添加剂对镍在玻璃碳上电结晶的影响

用阶跃电位法研究了光亮镍Watts镀液中添加剂:糖精、丁炔二醇和香豆素,对镍在玻璃碳上沉积初始阶段的影响.结果表明,在不含添加剂的该电解液,镍电结晶的初始阶段按连续成核的三维生长机理进行;分别或同时含有糖精和丁炔二醇时,则按瞬时成核的三维生长机理进行.糖精或丁炔二醇促进成核过程而阻化晶核的生长,这两种添加剂同时存在时则对成核和三维生长都起促进作用;添加香豆素或者同时添加三种添加剂不改变成核和生长的方式,但香豆素对成核过程和晶核生长都起着促进作用,而三种添加剂混用则对成核和生长过程起着阻化作用.本文采用Fleischmann等人郝二建模型.     

基于连接的频繁集聚类算法

针对大型事务数据库中频繁集的多属性聚类问题,提出一种高效的频繁集聚类算法.以往聚类算法采用基于距离的计算方法,由于受到属性数据的制约,在频繁集挖掘中具有一定的限制.在属性聚类基础上,基于连接对频繁集进行聚类.在算法中先找出数据点的邻居和计算相似度,构造邻居矩阵;然后计算连接数目,确定邻居数目矩阵;最后通过设置判定函数和阈值确定聚类数.通过实验证明,算法能够不仅能有效地完成频繁集的多属性聚类问题,而且还可以进一步发现频繁集在某一层次的相关性.     

Efficient algorithms for agglomerative hierarchical clustering methods

Whenevern objects are characterized by a matrix of pairwise dissimilarities, they may be clustered by any of a number of sequential, agglomerative, hierarchical, nonoverlapping (SAHN) clustering methods. These SAHN clustering methods are defined by a paradigmatic algorithm that usually requires 0(n ³) time, in the worst case, to cluster the objects. An improved algorithm (Anderberg 1973), while still requiring 0(n ³) worst-case time, can reasonably be expected to exhibit 0(n ²) expected behavior. By contrast, we describe a SAHN clustering algorithm that requires 0(n ² logn) time in the worst case. When SAHN clustering methods exhibit reasonable space distortion properties, further improvements are possible. We adapt a SAHN clustering algorithm, based on the efficient construction of nearest neighbor chains, to obtain a reasonably general SAHN clustering algorithm that requires in the worst case 0(n ²) time and space.Whenevern objects are characterized byk-tuples of real numbers, they may be clustered by any of a family of centroid SAHN clustering methods. These methods are based on a geometric model in which clusters are represented by points ink-dimensional real space and points being agglomerated are replaced by a single (centroid) point. For this model, we have solved a class of special packing problems involving point-symmetric convex objects and have exploited it to design an efficient centroid clustering algorithm. Specifically, we describe a centroid SAHN clustering algorithm that requires 0(n ²) time, in the worst case, for fixedk and for a family of dissimilarity measures including the Manhattan, Euclidean, Chebychev and all other Minkowski metrics.This work was partially supported by the Natural Sciences and Engineering Research Council of Canada and by the Austrian Fonds zur Förderung der wissenschaftlichen Forschung.     

采用k-均值聚类算法的资源搜索模型研究

针对当前海量信息存储对等网络系统中资源搜索技术效率较低的问题,提出了一种采用k-均值聚类分析的高效搜索模型.该模型利用资源描述框架(RDF)描述的元数据进行聚类分析,使得资源的搜索由全局变为局部,从而有效地提高了资源搜索效率;采用动态优化排序技术显著提高了查询的速度.通过子网分裂算法和节点备用算法增强了模型的可扩展性、安全性和可靠性.仿真结果表明,所提模型在查找时延和平均路径方面均比传统搜索模型更加高效、便捷.     

组蛋白修饰组合模式研究进展

核小体是染色质的基本结构单位,核小体组蛋白N末端尾部可以发生甲基化、乙酰化等多种共价修饰.组蛋白密码假设多种组蛋白修饰以组合方式发挥作用.自组蛋白密码假设被提出后,组蛋白修饰组合模式成为表观遗传学领域的重要研究内容.在染色质免疫沉淀基因芯片和免疫沉淀高通量测序等相关实验数据的基础上,多种算法被用于研究组蛋白修饰的组合.文章介绍了组蛋白修饰的发生、位点、相关修饰酶以及生物学功能,对组蛋白修饰组合以及与基因表达关系的研究进行了总结,同时对组蛋白修饰组合模式一些适用的研究方法做了概述和分析.