恐怖袭击事件记录数据的量化分析研究

基于GTD全球恐怖主义数据库,共研究四个方面的问题:对恐怖袭击事件分级,识别恐怖袭击事件制造者,分析未来反恐态势以及各个地区反恐应急能力.首先,建立了恐怖袭击事件危害性评价体系,运用层次分析法确定了指标权重,将危害程度分成了1-5等级;其次,采用基于遗传模拟退火算法的聚类算法,对选出的代表事件进行排序,基于灰色关联分析,分析了各聚类中心事件与要求事件的关联程度.再次,对近三年的恐怖袭击事件进行分类与预测分析.最后,通过建立模型,量化了恐怖组织单次恐怖袭击水平与地区反恐应急能力.结果表明:选取了近十年来十大恐怖事件大部分为国际恐怖事件,恐怖袭击事件与相关袭击事件制造者相关程度较高,全球恐怖袭击事件数量有所下降,应急能力最强的地区为西欧,最弱的为中亚,且恐怖袭击次数与反恐应急能力处于较强关联性.     

对恐怖袭击事件记录数据的量化分析

近年来,恐怖袭击愈演愈烈,合理分析判断恐怖袭击事件对于预防应急和安全救治可以发挥重大作用.根据用特征选择方法筛选出的主要属性,结合求出的属性权重构建了线性函数模型,以量化求出恐怖袭击事件的危害值,并以此构建恐怖袭击事件的量化分级模型.同时,还利用数据分析方法对恐怖袭击事件和反恐态势进行了分析.     

“对恐怖袭击事件记录数据的定量分析”评述

简述2018年中国研究生数学建模竞赛C题"对恐怖袭击事件记录数据的定量分析"的命题背景、数据来源和设计思路,并对参赛论文所涉及的有关问题和取得的成果进行评注.     

基于多模块模糊贝叶斯网络的恐怖袭击事件量化分析

近年来,全球范围内恐怖袭击事件的发生日益频繁,已经成为许多国家和地区所面临的主要安全问题.首先建立了全球恐怖袭击事件危险性分级指标体系,并基于多模块模糊贝叶斯网络来建立一个基于观测事件"后验概率"的推理模型,从而解决信息不确定时恐怖袭击事件的危险性分级.其次,利用FCM聚类模型对恐怖袭击事件的危险度进行聚类分析,从而依据事件特征对犯罪嫌疑人进行准确锁定.然后,建立了贝叶斯网络恐怖活动预测模型,为全球反恐态势提供有效预测,并利用ArcGIS热点图分析各地区恐怖袭击事件的时空特性.最后,基于VAR模型揭示了不同区域之间在恐怖袭击事件发生时跨区域间的相互影响关系,并提出了相应的反恐建议.     

基于灰色理论的恐怖袭击事件预测与分析

鉴于恐怖袭击事件的破坏性和危害性,对恐怖袭击事件进行分析和预测能够为相关部门组织反恐行动提供有效的理论支持,基于恐怖袭击事件发生的时间和地区因素,分析恐怖袭击事件的时空特性,建立了基于灰色理论的恐怖袭击事件预测模型,并根据恐袭事件季度性的特点,分季度对建立的模型进行了案例分析,通过计算和Matlab仿真证实了该模型的可行性,对反恐措施的制定具有一定的参考和指导意义.     

对恐怖袭击事件的多元统计分析

恐怖袭击威胁人类社会安全.选取全球恐怖主义数据库(Global Terrorism Database,GTD)中2015-2017年世界上发生的恐怖袭击事件的记录,根据相关性对2015-2016年未知作案组织或个人对应的事件数据进行整合简化,使用二阶聚类得到最大分类数,根据危害性从大到小选出前5个犯罪嫌疑人,利用判别分析对2017年未知作案组织或个人对应的事件进行概率预测,得出嫌疑人的嫌疑程度.选取影响恐怖袭击的重要指标,应用因子分析研究2015-2017年恐怖袭击事件发生规律,得到恐怖事件地域发展趋势.     

恐怖袭击事件的量化分析

首先基于近20年的全球恐怖主义数据库采用主成分分析和模糊综合评价模型对恐怖袭击事件的危害程度进行评价,对比分析后选取其中较优者即主成分分析模型作为评价结果,接着用极差等间隔分级法和剔除极端事件后极差平均间隔分级法对恐怖袭击的危害程度从高到低分为一至五级,综合比较后选取极差平均间隔分级法对其危害等级进行划分,该划分方法危害等级越小事件危害性越大.同时选取中国、美国、英国、印度、南非、澳大利亚6个典型国家,结合主成分综合评价值对其国家安全度进行定量评价.该评价及等级划分方法适用于对全球恐怖袭击事件进行危害评价及分级,同时可对任意国家的安全度进行评价.最后采用经纬度距离近似公式建立最短距离无约束最优化模型,给出反恐基地设置建议,可确定局部地区如中东地区的反恐基地位置.     

基于数据挖掘的全球恐怖主义数据库数据分析

运用数据挖掘的方法,对全球恐怖主义数据库(以下简称GTD)进行了量化分析.建立了基于KNN邻近算法的恐怖袭击事件量化分级模型和基于K-means聚类算法的恐怖袭击事件分类模型.此外,对近三年来恐怖袭击事件发生的主要原因、时空特性、蔓延特性以及级别分布规律进行了分析.最后,基于建立的模型和分析结论,对未来全球和某些重点地区的反恐态势进行了预测分析,给出了具有针对性的建议.     

抑郁症临床症状与事件相关电位的相关性统计分析

本文运用SAS软件中的二次响应面回归和聚类分析方法 ,研究了抑郁症临床症状与事件相关电位之间的相互关系。结果发现 ,HAMD总分与P2 波幅和P3波幅的联合作用呈显著的负相关 ,N1、P2 和N2 潜伏期或N1和P2 波幅或N2 和P3波幅各自成为一类     

房地产业上市公司的统计数据挖掘分析研究

应用统计类数据挖掘技术对房地产业上市公司财务进行了分析。即搜集十五个房地产业上市公司的主要财务指标进行因子分析,用提取主成份的方法缩减变数,归纳出影响公司财务状况的四个主要因素。然后,对十五家房地产上市公司进行聚类分析,划分各公司的经营等级。最后,结合因子分析与聚类分析的结果对各公司的经营状况进行了综合评价,并以此指导投资者和经营管理者做出正确的决策。     

Extending the participatory learning paradigm to include source credibility

We provide an overview of the participatory learning paradigm (PLP) and discuss the importance of the acceptance function in determining which observations are used for learning. We introduce a formal model that uses this (PLP) We then extend this model in two directions. First, we consider situations in which we have incomplete observations, we only have observations about a subset of the variables of interest. Next we extend this model to allow for the inclusion in the learning process of information about the learning agents belief about the credibility of the source of the learning experience. Here we distinguish between the content of a learning experience and the source of the experience. We provide a means to allow the learning agents belief about the credibility of the source to determine the effect of the content. Furthermore we suggest a method to allow the modification of agents belief about the credibility of the source to also be part of the learning process.     

Anomaly detection in a mobile communication network

Mobile communication networks produce massive amounts of data which may be useful in identifying the location of an emergency situation and the area it affects. We propose a one pass clustering algorithm for quickly identifying anomalous data points. We evaluate this algorithm’s ability to detect outliers in a data set and describe how such an algorithm may be used as a component of an emergency response management system.

Chameleon based on clustering feature tree and its application in customer segmentation

Clustering analysis plays an important role in the filed of data mining. Nowadays, hierarchical clustering technique is becoming one of the most widely used clustering techniques. However, for most algorithms of hierarchical clustering technique, the requirements of high execution efficiency and high accuracy of clustering result cannot be met at the same time. After analyzing the advantages and disadvantages of the hierarchical algorithms, the paper puts forward a two-stage clustering algorithm, named Chameleon Based on Clustering Feature Tree (CBCFT), which hybridizes the Clustering Tree of algorithm BIRCH with algorithm CHAMELEON. By calculating the time complexity of CBCFT, the paper argues that the time complexity of CBCFT increases linearly with the number of data. By experimenting on sample data set, this paper demonstrates that CBCFT is able to identify clusters with large variance in size and shape and is robust to outliers. Moreover, the result of CBCFT is as similar as that of CHAMELEON, but CBCFT overcomes the shortcoming of the low execution efficiency of CHAMELEON. Although the execution time of CBCFT is longer than BIRCH, the clustering result of CBCFT is much satisfactory than that of BIRCH. Finally, through a case of customer segmentation of Chinese Petroleum Corp. HUBEI branch; the paper demonstrates that the clustering result of the case is meaningful and useful. The research is partially supported by National Natural Science Foundation of China (grants #70372049 and #70121001).     

基于领域知识的纳税评估方法研究

纳税评估是税务稽查的基础和前提,科学的选案方法是纳税评估的关键。本文提出了一种基于领域知识的纳税评估方法。在领域知识的指导下,通过设计指标体系来提取个体的特征,进而采用聚类分析和统计分析相结合的方法,找出纳税异常的企业,从而完成纳税评估。     

Comment on “A new clustering approach using data envelopment analysis”

In a recent paper Po, Guh and Yang [Po, R.-W., Guh, Y.-Y., Yang, M.-S., 2009. A new clustering approach using data envelopment analysis. European Journal of Operational Research 199, 276–284] propose a new algorithm for forming clusters from the results of a DEA analysis. In this comment it is explained that the algorithm only generates information that is readily available from the usual DEA results.     

DEA based dimensionality reduction for classification problems satisfying strict non-satiety assumption

This study shows how data envelopment analysis (DEA) can be used to reduce vertical dimensionality of certain data mining databases. The study illustrates basic concepts using a real-world graduate admissions decision task. It is well known that cost sensitive mixed integer programming (MIP) problems are NP-complete. This study shows that heuristic solutions for cost sensitive classification problems can be obtained by solving a simple goal programming problem by that reduces the vertical dimension of the original learning dataset. Using simulated datasets and a misclassification cost performance metric, the performance of proposed goal programming heuristic is compared with the extended DEA-discriminant analysis MIP approach. The holdout sample results of our experiments shows that the proposed heuristic approach outperforms the extended DEA-discriminant analysis MIP approach.     

A study on the persistence of Farrell’s efficiency measure under a dynamic framework

This article introduces a sequence of four systematic methods to examine the extent to which the economic efficiency of Taiwan’s commercial banks persists and to uncover the potential dynamic link between bank performance and various financial indicators. Quasi-fixed inputs are explicitly incorporated in the DEA model to account for possible adjustment costs, regulation, or indivisibilities. Among the four methods, the dynamic panel data model and the Markov model appear to be exploited for the first time in the area of the DEA approach. Evidence is found that bank efficiency exhibits moderate persistence over the sample period, implying that the given sample banks fail to adjust their production techniques in a timely manner. Regulatory authorities and bank managers are suggested to be aware of the level of undesirable non-performing loans due to their close relationship with bank performance.     

基于区间数学方法的结构参数识别

统计能量分析方法是目前处理结构高频振动的有效方法之一,在航空、航天、船舶和机械等领域得到了广泛的应用.内损耗因子和耦合损耗因子是统计能量分析方法中非常重要的两个结构参数,这些参数可以利用试验测量得到的外部输入功率和结构模态能量通过理论方法识别出来.传统的统计能量分析参数识别方法没有考虑输入功率和模态能量的测量误差对识别结果的影响,识别结果精度较低,难以满足工程需要.该研究将区间数学方法应用于统计能量分析的参数识别,提出了一种可以考虑模态能量测量误差和输入功率测量误差的参数识别方法,揭示了模态能量和输入功率的测量误差对参数识别的影响规律,提高参数识别的准确性.该文的研究内容可以为后续的结构设计和安全性分析提供参考.     

On a Strategy to Develop Robust and Simple Tariffs from Motor Vehicle Insurance Data

The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features. The results of the strategy are a basis to develop insurance tariffs. We use a nonparametric approach based on a combination of kernel logistic regression and ε-support vector regression which both have good robustness properties. The strategy is applied to a data set from motor vehicle insurance companies.