基于决策树技术的信息化项目文化冲突研究

企业在信息化项目实施的各个环节中普遍存在各种各样的冲突．对冲突进行有效识别，依据冲突类型选择合适的对策．是降低项目风险、实现项目效益的关键。信息化项目的高复杂性、高风险性对项目的冲突管理提出了较高的要求．目前对信息化项目中的冲突管理研究较多集中于管理理念和理论分析层面。结合文化冲突特点的实证研究较少。决策树方法是数据挖掘及数据分析中广泛应用的技术。具有清晰直观、易于解释的优点。本文通过决策树C5．0算法，尝试运用数据挖掘技术进行文化冲突研究。通过对项目中6种冲突管理决策类型的分析，提供了依据冲突情景选择应对策略的分类决策方法。实验证明，该方法可行。本研究为我国企业在信息化项目中遇到的冲突问题。提供了应对方法，同时也扩展了数据挖掘决策树技术的应用领域。     

Coloured Logic Petri Nets and analysis of their reachable trees

Logic Petri nets (LPNs) can describe and analyse the batch processing function and passing value indeterminacy in cooperative systems, and alleviate the state space explosion problem. However, the indeterminate data of logical output transitions cannot be described explicitly in LPNs. Therefore, Coloured Logic Petri nets (CLPNs) are defined in this paper. It can determine the indeterminate data of logic output transitions in LPNs, i.e., the indeterminate data can be represented definitely in CLPNs. A vector matching method is proposed to judge the enabling transitions and analyse CLPNs. From the marking equation and the proposed reachable tree generation algorithm of CLPNs, a reachable tree can be built, and reachable markings are calculated. The advantage of CLPNs can be shown based on the number of leaf nodes of the reachability tree, and CLPNs can solve the indeterminate data of logical output transitions. Finally, an example shows that CLPNs can further reduce the dimensionality of reachable markings.     

一种基于线段树的动态规划优化算法

动态规划是解决多阶段决策最优化问题的一种思想方法，也是ACM程序设计竞赛中常用的算法。本文首先讨论了动态规划的基本思想和解题步骤。但基本动态规划对于数据规模很大的问题，在解题过程中还是存在效率和占用空间非常大的问题，本文巧妙利用线段树优化动态规划，提高对大规模数据处理的方法和技巧，在线段树基础上利用树状数组合理地解决了动态规划占用大量内存的问题。     

关于大坝风险的Vague集事故树的评价模型

本模型主要从对大坝风险评价的角度出发,对导致大坝风险的元素及其逻辑关系进行分析。首先,利用可靠度的知识对大坝系统的失效概率进行分析。然后,结合事故树模型,使大坝的风险元素间的逻辑关系多元化,并对风险元素间的关系应用Vague集理论进行模糊化处理,利用客观数据进行评价,使评价更加客观。最后,模型给出了评价的结果,并进行了讨论。     

基于激光雷达点云的树冠分形维数及其算法实现

本文以地面激光雷达点云数据为基础,利用盒计数维数法计算单个树冠的分形维数,并对其进行了自相似特征研究。实践证明了该计算方法的可行性。     

Novel ID-based anti-collision approach for RFID

Novel correlation ID-based (CID) anti-collision approach for RFID under the banner of the Internet of Things (IOT) has been presented in this paper. The key insights are as follows: according to the deterministic algorithms which are based on the binary search tree, we propose a method to increase the association between tags so that tags can initiatively send their own ID under certain trigger conditions, at the same time, we present a multi-tree search method for querying. When the number of tags is small, by replacing the actual ID with the temporary ID, it can greatly reduce the number of times that the reader reads and writes to tag’s ID. Active tags send data to the reader by the way of modulation binary pulses. When applying this method to the uncertain ALOHA algorithms, the reader can determine the locations of the empty slots according to the position of the binary pulse, so it can avoid the decrease in efficiency which is caused by reading empty slots when reading slots. Theory and experiment show that this method can greatly improve the recognition efficiency of the system when applied to either the search tree or the ALOHA anti-collision algorithms.     

Analysing Multivariate Spatial Point Processes with Continuous Marks: A Graphical Modelling Approach

This paper discusses the application of graphical modelling in the statistical analysis of marked point patterns. We consider a multivariate planar point process with quantitative marks. After a survey of statistical methods for marked point processes, a new graphical model is presented. The sub‐processes of marked points with identical discrete marks are identified with nodes of a graph, which is used to describe aspects of the spatial relationship: If two sub‐patterns are similar, then an arc is made between the corresponding nodes. Similarity is defined based on spectral densities, which makes the computations efficient. The resulting graph presents all these pairwise similarities simultaneously. We demonstrate the application of our method in the analysis of a multi‐species forest, where the points are tree locations, the discrete marks tree species and the quantitative marks are the diameters at breast height.     

一个新的银行信用风险评估方法

本文以银行信用风险管理为例，将粗糙集和决策树两种具有互补优势的数据挖掘方法相结合，对客户信用做出归类分析判断，最后利用决策树生成决策规则。实践证明，这种方法忠于原始数据，提高了分类准确度。减小了决策树规模，具有良好的性能。     

基于改进的SVM的交通信息融合方法研究

在对交通数据特性分析的基础上,提出基于改进的SVM多源交通信息融合算法。首先,在分析了传统SVM不足的基础上,提出了决策树-支持向量机算法(Decision TreeMethod-SupportVector Machines,DTM-SVM)。其次,对DTM-SVM多源信息融合算法的模型及其流程进行了研究,建立了DTM-SVM的分层结构模型。最后,经过实际数据测试,证明了该方法的高效性和实用性。     

Best classification algorithms in peer-to-peer lending

A proper credit scoring technique is vital to the long-term success of all kinds of financial institutions, including peer-to-peer (P2P) lending platforms. The main contribution of our paper is the robust ranking of 10 different classification techniques based on a real-world P2P lending data set. Our data set comes from the Lending Club covering the 2009–2013 period, which contains 212,252 records and 23 different variables. Unlike other researchers, we use a data sample which contains the final loan resolution for all loans. We built our research using a 5-fold cross-validation method and 6 different classification performance measurements. Our results show that logistic regression, artificial neural networks, and linear discriminant analysis are the three best algorithms based on the Lending Club data. Conversely, we identify k-nearest neighbors and classification and regression tree as the two worst classification methods.     

堆垛机故障诊断专家系统的知识获取与表示

堆垛机的可靠性和可维护性是提高作业效率、保证设备良好工况的重要因素。文中通过分析堆垛机故障发生的机理,结合故障树分析法,建立了系统的故障树,并用故障树表示故障原因及其故障传播逻辑关系,从故障树的顶事件至底事件形成故障分析支路,并进一步建立了堆垛机工作中可能发生的各种异常状态的知识库,故障诊断专家系统的知识获取采用人工获取的方式,知识表示使用产生式规则。     

决策树算法在基本医疗保险中的应用研究

随着计算机技术以及学科交叉的发展,数据挖掘技术被越来越多应用到保险的风险分析中。文章使用数据挖掘基本算法之一的决策树算法分析研究了某市的基本医疗保险数据样本,找出隐含的有效信息,并在此基础上提出了完善基本医疗保险的对策建议。     

一种树形目录继承权限功能的数据模型

文章通过对树形目录继承权限功能的特点进行分析,定义了一种通用的树形目录继承权限功能数据模型。该数据模型应用广泛,不限定编程语言,应用系统架构和具体的数据库类型,只要满足模型定义的数据表结构、视图和表值函数,即可简单快速地在系统中实现树形目录继承权限功能。     

States of psychological anchors and price behavior of Japanese yen futures

This study explores whether the relationship between Japanese yen futures returns and the corresponding equity returns is affected by the states of psychological anchors of the currency and stock markets. This study employs the linear-regression-based tree model (a machine learning method) to account for the framing effect of the anchors. The empirical results of the linear-regression-based tree model show that the currency price behaviors of momentum and reversal, and prediction by equity markets, vary with the anchors. Empirical evidence also indicates that the linear-regression-based tree model outperforms the OLS model based on the estimation results and out-of-sample forecasting. The forecasting performance of the linear-regression-based tree model can be improved along with an increase in the forecasting period.     

数据挖掘技术在快递品牌选择上的应用

以影响客户选择快递品牌的年龄、收入等七个因素数据为研究对象,以数据挖掘方法中的分类算法中的J48为技术手段建立模型并得到研究对象的决策树,通过对决策树的分析,得到影响客户选择快递品牌的相关条件,并把这些条件提供给各品牌快递公司以供其有针对性的选择和发展客源。     

基于决策树的全日制本科生英语四级考试通过分析

我国大学生为通过大学英语四级考试需要付出很大的时间代价,分析影响大学英语四级考试通过的原因,对于采取针对性措施通过英语四级考试意义重大。以某特色地方工科高校教学管理系统为研究对象,从中提取了四个学院的学生成绩数据,采用决策树方法进行了分析。数据分析结果表明：大学英语课程成绩对大学英语四级考试通过影响最大,毕业设计成绩、高等数学课成绩、专业基础必修课成绩也会影响大学英语四级考试的通过,基础必修课成绩对大学英语四级考试通过的影响不大。     

A semantics-based method for clustering of Chinese web search results

Information explosion is a critical challenge to the development of modern information systems. In particular, when the application of an information system is over the Internet, the amount of information over the web has been increasing exponentially and rapidly. Search engines, such as Google and Baidu, are essential tools for people to find the information from the Internet. Valuable information, however, is still likely submerged in the ocean of search results from those tools. By clustering the results into different groups based on subjects automatically, a search engine with the clustering feature allows users to select most relevant results quickly. In this paper, we propose an online semantics-based method to cluster Chinese web search results. First, we employ the generalised suffix tree to extract the longest common substrings (LCSs) from search snippets. Second, we use the HowNet to calculate the similarities of the words derived from the LCSs, and extract the most representative features by constructing the vocabulary chain. Third, we construct a vector of text features and calculate snippets’ semantic similarities. Finally, we improve the Chameleon algorithm to cluster snippets. Extensive experimental results have shown that the proposed algorithm has outperformed over the suffix tree clustering method and other traditional clustering methods.     

Incorporating support vector machines with multiple criteria decision making for financial crisis analysis

Feature selection is an essential pre-processing technique in data mining that eliminates redundant or unrepresentative attributes and improves the performance of classifiers. However, a classifier with different feature selection approaches results in diverse outcomes. Thus, determining how to integrate feature selection methods and yield an appropriate feature set is an issue worth further study. Based on ensemble learning, this investigation develops a SVMMCDM (support vector machines with multiple criteria decision making) model that employs various feature selection techniques as data preprocessing schemes and then uses SVM for financial crisis prediction. The study uses MCDM to determine the most suitable feature selection mechanism when many performance criteria are considered. After the feature selection mechanism has been determined, the study decomposes the SVM to obtain support vectors and predicted labels which are then fed into a decision tree to generate rules. The numerical results for the ex-ante and ex-post periods relative to the financial tsunami show that the proposed SVMMCDM model is an effective way to predict a financial crisis and can provide useful rules for decision makers.     

基于聚类分析的数据挖掘方法在CRM中的应用研究

客户关系管理是一项营销策略,透过选择和管理客户达到最大的长期价值。它按照客户细分情况有效地组织企业资源,培养以客户为中心的经营行为以及实施以客户为中心的业务流程,以此为手段来提高企业的获利能力、收入以及客户满意度。文章介绍了用挖掘理论对企业CRM进行效能分析,通过表属性的数据收集,用矩阵分析法和最大树法对表象数据进行聚类筛选,取得了较为满意的效果。     

用二叉树解决配送线路问题

物流配送是物流中的核心环节,配送成本在整个物流过程费用中占有很大的比重,因此为了减少配送成本有必要对其配送路线进行合理优化。运用二叉树遍历的知识并结合节约算法的思想,将货物需求点作为叶子结点并适当增加一些需求量为零的叶子结点构造一种有特殊意义的二又树,提出了一种运用这种特殊二叉树在满足车辆额定载货量的前提下寻求最优配送路线的方法．并通过实例证明了其正确性。