Numerical taxonomy and the principle of maximum entropy

The standard procedure in numerical classification and identification of micro-organisms based on binary features is given a justification based on the principle of maximum entropy. This principle also strongly supports the assumption that all characteristics upon which the classification is based are equally important and the use of polythetic taxa. The relevance of the principle of maximum entropy in connection with taxonomic structures based on clustering and maximal predictivity is discussed. A result on asymptotic separateness of maximum entropy distributions has implications for minimizing identification errors.The work was partially supported by the Bank of Sweden Tercentenary Foundation, The Swedish Council for Forestry and Agricultural Research, The Carl Trygger Foundation, and the Swedish Cancer Foundation.     

New clustering algorithm for interconnection of MANET and internet

1 .INTRODUCTIONRecentyearshavewitnessedtherapiddevelopmentsinmobilecommunicationandmultimedianetworktechniques.Thesetrendsaremotivatingagreatdealofinterestsinmakingsurethatmobilenetworkscanaccessinfrastructurenetworks (especiallyInternet)forthepurposeofex…     

基于通讯网络安全模糊聚类与量子遗传理论的入侵检测研究

提出了一种新的自适应的检测算法——量子遗传模糊聚类算法（QGFC）.该算法利用量子遗传理论,在无监督的条件下,通过模糊聚类的方法对数据集进行自动分类,以达到自主识别入侵行为的目的.实验仿真结果显示,此算法可以有效地对入侵行为进行检测.     

基于灰色聚类的管网水质评价

管网水质可以视为一个灰色系统,运用灰色聚类方法可对管网水质进行评价。灰色聚类方法克服了传统的用单一值评价多因素多指标问题的弊病。以广州市管网水质为实例,对其管网水质进行评估。结果表明,灰色聚类方法可采用数量较少的样本对管网水质的等级进行评估,从而为各测点水质状况信息的获取提供了便利。     

一种稳定的基于位置信息的分簇路由协议

由于车载自组织网络具有节点高速移动,网络拓扑频繁变化,链路不稳定等特点,直接使用移动自组网的路由协议,将会产生传输延时增大,丢包率上升等一系列的问题,为此本文提出一种稳定的、基于位置信息的分簇路由协议（Stable Clustering and Position-base Routing）,该协议以节点的运动特性作为分簇依据,利用节点的地理位置信息和电子地图信息进行路由决策。仿真结果表明,该协议能有效地改善簇结构的稳定性,提高数据传输的实时性,降低网络的丢包率。     

杂合灰色聚类与变精度粗糙模糊集的概率决策方法

在基于粗糙集理论或扩展粗糙集理论的不确定性管理决策中,仅由数据集无法获得决策表,同时决策属性呈现模糊性,以至于无法获取概率决策规则。鉴于此,本文构建了灰色定权聚类与粗糙集变精度的杂合模型,首先运用基于中心点三角白化权函数的灰色定权聚类方法对包含不同量纲指标的知识表示系统进行处理,生成多属性决策表,而后运用变精度粗糙模糊集进行决策分析,获取概率决策规则。最后以区域关键技术选择为例,表明该模型的可行性与实用性。     

基于聚类分组的P2P网络信任模型

由于P2P网络的匿名性与节点自由进出P2P网络的特点,给网络带来了一系列的安全问题。为了解决P2P网络中的信任问题．本文提出了一种基于聚类分组的信任模型。首先给出了信任度的定义;然后依据兴趣分组计算节点的信任值和小组的信任值;最后提出了基于聚类分组的信任模型的实现方式。     

基于DBSCAN算法的恐怖主义风险评估模型——以铁路系统为例

为了对铁路系统涉恐事件进行风险管理,遏制铁路系统恐怖袭击事件的发生,提出基于DBSCAN(density-based spa-tial clustering of applications with noise)算法的铁路系统恐怖袭击风险评估方法.首先对1970—2017年发生的铁路系统恐怖袭击案件进行统计分析,然后采用DBSCAN算法对恐怖袭击发生次数、死亡人数和受伤人数3项风险评价指标进行聚类分析,最终客观计算出几类袭击方式、袭击目标和86个国家的风险.结果表明,该方法的分析过程避免了人工赋值和专家打分策略,评估结果更具客观性和真实性,适用于反恐情报工作的风险评估领域.     

Large-sample results for optimization-based clustering methods

Many common (nonhierarchical) clustering and classification methods are optimization-based methods, in the sense described by Windham (1987) in this Journal. This paper gives some large sample properties for estimates derived by such methods. Under appropriate conditions, such estimates converge with probability one to a limit, and are asymptotically normally distributed around that limiting value. The conditions are satisfied by most of the common examples of optimization-based methods. Prepared for the 2nd International Conference, International Federation of Classification Societies, Charlottesville, VA, 1989. Supported in part by summer research funds, Graduate School of Business Administration, University of Colorado at Denver.