有根系统发生树的精确有效比较

系统发生树代表了不同物种之间进化关系的历史。生物信息学中的一个基本问题是对系统发生树进行比较。一种比较方法是通过定义树空间中两棵系统发生树之间的相似度或相异度来测定这两棵树的同异。Robinson-Foulds距离是目前使用最广泛的相异度。定义了一个用于有根系统发生树比较的新的相异度,该相异度考虑了子类间更精细的相似,而不是如Robinson-Foulds距离那样仅考虑子类相同与否,因此能够提供更精确、清晰的测量。给出了两个能有效计算这个相异度的算法。简单修改之后,这些结果适用于其他5个相关的比较指标。     

基于模型结构和事件日志的流程相似度计算

流程相似度的计算在企业业务流程管理中具有重要作用。目前相似度的计算主要存在两个问题：一是大多数相似度计算方法只考虑模型结构或事件日志,导致算法不够精确;二是综合考虑了模型结构和事件日志的算法复杂度高且效率低。因此,提出了一种改进的流程模型结构和事件日志相结合的方法。首先将流程模型结构中的紧邻活动转化为邻接矩阵,然后根据事件日志中的行为信息对邻接矩阵进行加权得到加权邻接矩阵,最后采用符合距离度量特性的矩阵间距离的算法来度量流程间相似度。通过实验与MDS、GED以及WBPG等算法进行对比,所提方法的准确率更高,为99.51%,计算效率也更高。     

基于云模型的改进FMEA风险评估方法

针对风险评估过程中的模糊性和随机性两个不确定性因素,采用云模型对语义评价变量进行量化,并通过云的合成对专家的评价结果进行集结,构建云风险评价矩阵。将风险因子O、S、D期望重要度与云的相似度相结合计算风险因子的权重。考虑到传统故障模式与影响分析（Failure Mode and Effects Analysis,FMEA）中故障模式风险值排序的缺陷,基于云的距离测度算法提出云-VIKOR的风险排序方法。最后以实例验证了所提方法的精确性和有效性。     

一种新的Vague集距离测度及其应用

在给出Vague集及其距离测度的概念后,将单个Vague值作为一个区间,运用Vague集向Fuzzy集转化的思想将区间离化为点,通过求点的欧式距离得出两个Vague值之间的距离测度。分析了该公式的性能,并讨论了它在模糊识别中的应用。     

云模型与VIKOR集成的多属性群决策方法

针对以往定性概念量化过程中只考虑到模糊性这一不确定性因素的不足,采用云模型从模糊性和随机性两个方面实现语义评价变量的量化,并对专家组的语义评价信息进行集结。针对以往云差异性度量方法的不足,基于云模型构成的本质特点,从云滴分布的角度提出了云距离测度算法,进而提出了云相似度算法。考虑到决策指标间相互影响关系,将云模型与决策试验与实验评估法（Decision-Making Trial and Evaluation Laboratory,DEMATEL）相结合,采用云DEMATEL法对专家主观评价给出的指标初始重要度进行修正,进而计算得到指标权重。采用云距离算法计算备选方案与正、负理想解间的距离,并最终由云VIKOR求得备选方案的妥协解。最后以实例验证了所提方法的可行性和有效性。     

基于Levenshtein distance算法的句子相似度计算

基于Levenshtein distance(LD)算法,以计算句子相似度为切入点,通过实验分析对自然语言处理过程中如何计算两个句子的相似度进行研究。实验证明在度量两个句子相似度的计算方法中Levenshtein distance(LD)算法是度量两个句子相似度的有力算法或工具,有助于在拼写检查、雷同试卷分析等程序开发过程中编写出更加高效的代码。     

Value集的模糊嫡、相似度量和距离测度的关系

Vague集理论在各个领域中的广泛应用引起越来越多学者的注意,而模糊墒、相似度量和距离测度是其中的3个关键技术。目前已提出多种关于Vague集的模糊嫡、相似度量和距离测度的计算方法,但这些研究都没有讨论这3个基本概念之间的联系。基于Vague集的模糊嫡、相似度量和距离测度的公理化定义,给出了三者之间的相互诱导关系,建立了模糊墒、相似度量和距离测度之间的联系。     

基于6 Sigma的流程自优化系统研究

近年来，国内外的企业都非常流行业务流程重组(BPR)。刚时，作为买现业务流程集成的有效途径之一的工作流技术（Wfm)也扩大到了多个领域。但是，现有的WfM技术和BPR并没有很好的结合起来。为了通过工作流系统的实施来有效的实现企业的BPR，有必要开发基于企业业务流程的工作流系统。通过引入最新的质量管理思想——6 Sigma理论，对业务流程数据进行分析和统计，并且根据工作流系统的特点，设计出具有智能报警功能和流程优化决策方案的流程自优化系统。     

MIS系统开发中流程控制的设计与实施

本文介绍了MIS系统开发过程中流程控制中技术难题的两种设计与实现方法及其优缺点。为使流程的应用更加灵活、方便,提出了用户自定义业务流程的设想。     

信息安全能力成熟度模型(IS-CMM)的建构

在能力成熟度模型(CMM)的基础上提出“信息安全能力成熟度模型”(IS—CMM)这一构想，并着重探讨了IS—CMM各级中的核心流程域KPAs的构建。     

An empirical study on improving dissimilarity-based classifications using one-shot similarity measure

This paper reports an experimental result obtained by additionally using unlabeled data together with labeled ones to improve the classification accuracy of dissimilarity-based methods, namely, dissimilarity-based classifications (DBC) [25]. In DBC, classifiers among classes are not based on the feature measurements of individual objects, but on a suitable dissimilarity measure among the objects instead. In order to measure the dissimilarity distance between pairwise objects, an approach using the one-shot similarity (OSS) [30] measuring technique instead of the Euclidean distance is investigated in this paper. In DBC using OSS, the unlabeled set can be used to extend the set of prototypes as well as to compute the OSS distance. The experimental results, obtained with artificial and real-life benchmark datasets, demonstrate that designing the classifiers in the OSS dissimilarity matrices instead of expanding the set of prototypes can further improve the classification accuracy in comparison with the traditional Euclidean approach. Moreover, the results demonstrate that the proposed setting does not work with non-Euclidean data.     

基于改进流形距离K-medoids算法

针对空间分布复杂的数据以及空间分布未知的现实数据聚类问题,设计了一种改进流形距离作为不相似测度。该不相似测度可有效利用所有数据点之间的全局一致性,挖掘无类属数据集的空间分布信息。通过使用该不相似测度,提出了基于改进流形距离K-medoids算法。将新算法与基于已有的流形距离和基于欧氏距离的K-medoids算法进行性能比较,对八个人工数据集以及USPS手写体数字识别问题的实验结果表明:新算法针对不同结构的测试数据集,在聚类性能上均优于或接近于另外两种K-medoids算法,并且对于各种分布的,无论简单或复杂,凸或者非凸的数据都可以进行聚类。     

一种个体软件过程能力度量方法

个体软件过程(PSP)是由卡内基×梅隆大学软件工程研究所的Humphrey领导开发的.它是一种可用于控制、管理和改进个人工作方式的自我持续改进过程.随着工业界对软件过程改进需求的日益增长,PSP成为了软件组织为达成完全(从宏观到微观)量化过程管理研究中的一个热点课题.软件过程研究表明,高水平的个体软件过程能力是软件项目成功的关键,如何进行有效的个体软件过程能力度量是PSP中的一个核心问题.现有方法不能同时有效处理个体软件过程能力度量中的可变规模收益、多变量输入/输出以及决策者偏好问题.提出了一种综合了数据包络分析(DEA)和层次分析法(AHP)的个体软件过程能力评价方法——PSPADA,介绍了PSPADA的个体软件过程能力评价模型和核心算法(集成决策者偏好和估计规模收益).实验结果显示,PSPADA能够在考虑决策者偏好的同时,有效地进行多指标、规模收益可变的量化评估.     

A workflow net similarity measure based on transition adjacency relations

Many activities in business process management, such as process retrieval, process mining, and process integration, need to determine the similarity or the distance between two processes. Although several approaches have recently been proposed to measure the similarity between business processes, neither the definitions of the similarity notion between processes nor the measure methods have gained wide recognition. In this paper, we define the similarity and the distance based on firing sequences in the context of workflow nets (WF-nets) as the unified reference concepts. However, to many WF-nets, either the number of full firing sequences or the length of a single firing sequence is infinite. Since transition adjacency relations (TARs) can be seen as the genes of the firing sequences which describe transition orders appearing in all possible firing sequences, we propose a practical similarity definition based on the TAR sets of two processes. It is formally shown that the corresponding distance measure between processes is a metric. An algorithm using model reduction techniques for the efficient computation of the measure is also presented. Experimental results involving comparison of different measures on artificial processes and evaluations on clustering real-life processes validate our approach.     

一种基于同步合成构造Petri网进程表达式的方法

Petri网的进程是用于系统行为和状态描述的有效工具,Petri网的进程表达式可以给出系统全部进程的描述,但是对于任意无界Petri网而言求取其进程表达式十分困难.文中首先考察结构简单的S-网的进程行为,给出各种类型的S-网的进程表达式的描述方法.然后拓展了Petri网同步合成的概念,分析了同步合成过程中基本进程段集之间的关系,并利用同步混排给出了进程表达式之间的关系.随后证明了一个Petri网可以通过一组S-网同步合成得到,利用S-网的进程表达式给出了构造Petri网的进程表达式的方法.     

Combination of sources of evidence with different discounting factors based on a new dissimilarity measure

The sources of evidence may have different reliability and importance in real applications for decision making. The estimation of the discounting (weighting) factors when the prior knowledge is unknown have been regularly studied until recently. In the past, the determination of the weighting factors focused only on reliability discounting rule and it was mainly dependent on the dissimilarity measure between basic belief assignments (bba's) represented by an evidential distance. Nevertheless, it is very difficult to characterize efficiently the dissimilarity only through an evidential distance. Thus, both a distance and a conflict coefficient based on probabilistic transformations BetP are proposed to characterize the dissimilarity. The distance represents the difference between bba's, whereas the conflict coefficient reveals the divergence degree of the hypotheses that two belief functions strongly support. These two aspects of dissimilarity are complementary in a certain sense, and their fusion is used as the dissimilarity measure. Then, a new estimation method of weighting factors is presented by using the proposed dissimilarity measure. In the evaluation of weight of a source, both its dissimilarity with other sources and their weighting factors are considered. The weighting factors can be applied in the both importance and reliability discounting rules, but the selection of the adapted discounting rule should depend on the actual application. Simple numerical examples are given to illustrate the interest of the proposed approach.     

聚类分析中的差异性度量方法研究

距离与差异性度量是聚类分析中的基本概念,是许多聚类算法的核心内容。在经典的聚类分析中,度量差异性的指标是距离的简单函数。该文针对混合属性数据集,提出两种距离定义,将差异性度量推广成为距离、类大小等因素的多元函数,使得原来只适用于数值属性或分类属性数据的聚类算法可用于混合属性数据。实验结果表明新的距离定义和差异性度量方法可提高聚类的质量。     

面向分类属性数据的一种改进相异性度量及其在客户细分中的应用*

具有层次结构的分类属性在客户细分应用中广泛存在。针对传统相异性度量无法准确反映决策者在与细分目标相关的决策指标上的偏好信息,提出一种改进的距离层次并给出使用该度量,基于聚类分析的客户细分基本流程。该度量利用距离层次计算各分类属性值概念间的相异性,同时引入指标距离的概念描述对于特定指标,决策者在不同分类属性值上的偏好,结合模糊相似优先比决策方法和树的广度优先遍历计算不同分类属性值间的指标距离,最后通过将所求得的概念距离和指标距离进行加权求和以更全面地度量不同分类属性值间的相异性。对陕西省电力公司工业客户进行细分实验的结果表明：与传统距离层次相比,采用改进相异性度量能提高聚类质量和细分结果的可解释性。     

ASTRA - A Novel interest measure for unearthing latent temporal associations and trends through extending basic gaussian membership function

Time profiled association mining is one of the important and challenging research problems that is relatively less addressed. Time profiled association mining has two main challenges that must be addressed. These include addressing i) dissimilarity measure that also holds monotonicity property and can efficiently prune itemset associations ii) approaches for estimating prevalence values of itemset associations over time. The pioneering research that addressed time profiled association mining is by J.S. Yoo using Euclidean distance. It is widely known fact that this distance measure suffers from high dimensionality. Given a time stamped transaction database, time profiled association mining refers to the discovery of underlying and hidden time profiled itemset associations whose true prevalence variations are similar as the user query sequence under subset constraints that include i) allowable dissimilarity value ii) a reference query time sequence iii) dissimilarity function that can find degree of similarity between a temporal itemset and reference. In this paper, we propose a novel dissimilarity measure whose design is a function of product based gaussian membership function through extending the similarity function proposed in our earlier research (G-Spamine). Our approach, MASTER (Mining of Similar Temporal Associations) which is primarily inspired from SPAMINE uses the dissimilarity measure proposed in this paper and support bound estimation approach proposed in our earlier research. Expression for computation of distance bounds of temporal patterns are designed considering the proposed measure and support estimation approach. Experiments are performed by considering naïve, sequential, Spamine and G-Spamine approaches under various test case considerations that study the scalability and computational performance of the proposed approach. Experimental results prove the scalability and efficiency of the proposed approach. The correctness and completeness of proposed approach is also proved analytically.