关系数据库中带语言值权重的模糊查询

在模糊理论的基础上,提出了将权重概念引入数据库模糊查询中,使用户对查询中各个属性的相对重视程度得以体现。为每条记录提供了一个匹配度,按匹配度的降序输出结果,方便用户选择。权重和匹配度都是语言变量［7］,其取值为语言值形式,更加贴近自然。     

语言区间直觉模糊Frank算子

针对语言区间直觉模糊信息的集结问题,文中提出Frank集结算子,并构建解决供应商选择问题的群决策方法.首先引入拓展Frank t-模与s-模定义语言区间直觉模糊集的Frank运算法则,提出语言区间直觉模糊Frank加权平均(LIVIFFWA)算子与几何(LIVIFFWG)算子,证明算子的幂等性、封闭性、单调性等基本性质,剖析算子关于参数的退化性.然后,基于LIVIFFWA算子与LIVIFFWG算子构建语言区间直觉模糊多属性群决策方法,用于解决供应商决策问题.最后,通过共享单车回收供应商选择的案例分析验证文中决策方法的可行性和灵活性,讨论参数变化对决策结果的影响,并验证参数具有表征和反馈决策者态度的能力.     

模糊传感器语言概念生成实用方法

在简要介绍扩张原理基础上，讨论了模糊传感器语言概念生成的实用方法。     

基于语言值有序对三元组的多类型数据标准化方法

为了标准化处理多类型数据,基于语言值二元组,提出语言值有序对三元组表示模型,研究语言值有序对三元组及其性质,给出语言值有序对三元组之间的相似度.讨论语言值有序对三元组加权平均算子,构造模糊集、直觉模糊集、犹豫模糊集、语言真值直觉模糊集与语言值有序对三元组的标准化转化模型.结合语言值有序对三元组之间的相似度,将模糊集、直觉模糊集、犹豫模糊集、语言真值直觉模糊集的转化模型应用于模式识别中.最后,通过医院智能分诊的实例说明文中方法的合理性和有效性.     

基于语义表示的数据仓库构建方法

介绍了基于语义的数据仓库的构建方法,给出了3层数据加工流程的数据加工实例,在基于语义的数据仓库技术基础上,提出了一种新的支持模糊查询的模糊数据的管理方法,将其应用于某烟草销售企业数据仓库的构建,取得了较好的效果.     

Linguistic majorities with difference in support

In social choice voting, majorities based on difference of votes and their extension, majorities based on difference in support, implement the crisp preference values (votes) and the intensities of preference provided by voters when comparing pairs of alternatives, respectively. The aim of these rules is declaring which alternative is socially preferred and to that, they require the winner alternative to reach a certain positive difference in its social valuation with respect to the one reached by the loser alternative. This paper introduces a new aggregation rule that extends majorities based on difference of votes from the context of crisp preferences to the framework of linguistic preferences. Under linguistic majorities with difference in support, voters express their intensities of preference between pairs of alternatives using linguistic labels and an alternative defeats another one when a specific support, fixed before the election process, is reached. There exist two main representation methodologies of linguistic preferences: the cardinal one based on the use of fuzzy sets, and the ordinal one based on the use of the 2-tuples. Linguistic majorities with difference in support are formalised in both representation settings, and conditions are given to guarantee that fuzzy linguistic majorities and 2-tuple linguistic majorities are mathematically isomorphic. Finally, linguistic majorities based on difference in support are proved to verify relevant normative properties: anonymity, neutrality, monotonicity, weak Pareto and cancellativeness.     

基于二元对比插值原理的语言概念生成方法

在介绍二元对比排序方法基础上，提出一种用于模糊传感器的二元对比插值语言概念生成方法。这种语言概念生成方法特别适用于已获得特征测量点情况，主要特点是易于实现有导师学习。     

The performance of recommender systems in online shopping: A user-centric study

This research investigates the effects of preference relaxation on decision-making performance of users in online preference-based product search contexts. We compare four recommender systems based on different preference relaxation methods in extensive user experiments with 111 subjects that use two real-world datasets: 1818 digital cameras and 45,278 used car advertisements gathered from popular e-commerce websites. Our results provide new insights into the positive impact of the Soft-Boundary Preference Relaxation methods on decision-making quality and effort. The paper extends previous studies on this topic and demonstrates that decision aids based on preference relaxation techniques can effectively enhance preference-based product search in online product catalogues and help alleviate common disadvantages of form-based filtering mechanisms.     

Linguistic modelling of imperfect spatial information as a basis for simplifying spatial analysis

Spatial analysis is at the basis of several decision processes in fields such as urban planning and environmental management. In order to carry it out effectively more flexible Geographic Information Systems are needed to be able to represent and manage the imperfection that invariably affects geographic information. Starting from the consideration that experts usually deal with imperfect spatial information through linguistic terms (e.g., they identify the approximate position of a phenomenon on a map and classify spatial properties through linguistic labels), this contribution proposes the use of linguistic granule of information to represent and manage imperfect spatial information. A fuzzy object-based data model is proposed as a tool for supporting spatial analysis based on the management of linguistic granule. In particular, the problem of defining methods to manage imperfect information depending on the type of imperfection is discussed. Finally, an example of spatial analysis applied to support a decision problem in environmental impact assessment is described.     

SOME PROCEDURES FOR SELECTING FUZZY SET-THEORETIC OPERATORS

Abstract

We investigate the definition of the “and” operation in multivalued logic and decision making. A set of basic properties are suggested. Rules for selecting an appropriate operator are given. A general class of connectives is also suggested.     

A recommender system to avoid customer churn: A case study

A major concern for modern enterprises is to promote customer value, loyalty and contribution through services such as can help establish a long-term, honest relationship with customers. For purposes of better customer relationship management, data mining technology is commonly used to analyze large quantities of data about customer bargains, purchase preferences, customer churn, etc. This paper aims to propose a recommender system for wireless network companies to understand and avoid customer churn. To ensure the accuracy of the analysis, we use the decision tree algorithm to analyze data of over 60,000 transactions and of more than 4000 members, over a period of three months. The data of the first nine weeks is used as the training data, and that of the last month as the testing data. The results of the experiment are found to be very useful for making strategy recommendations to avoid customer churn.     

Measuring the success possibility of implementing ERP by utilizing the Incomplete Linguistic Preference Relations

This paper applies an analytic hierarchical prediction model based on the Multi-Criteria Decision Making with Incomplete Linguistic Preference Relations (InLinPreRa) to help the organizations become aware of the essential factors affecting the Enterprise Resource Planning (ERP), as well as identify the actions necessary before implementing ERP. The subjectivity and vagueness in the prediction procedures are dealt with linguistic variables quantified in an interval [−t, t]. Then predicted success/failure values are obtained to enable organizations to decide whether to initiate ERP, inhibit adoption or take remedial actions to increase the success possibility of ERP. Pairwise comparisons are used to determine the priority weights of influential factors, and the possible occurrence ratings of success or failure outcome amongst decision makers. There are not any inconsistency occurred in this procedures because this proposed approach allows every decision expert to choose an explicit criterion or alternative for the without restriction. When there are n criteria in a decision matrix, only n − 1 times of pairwise comparisons are taken. This approach not only improves the efficiency of pairwise comparison compared with the traditional AHP, but also avoids the checking the consistency of linguistic preference relation when the decision makers undertake the pairwise comparison processes.     

Linguistic database summaries and their protoforms: towards natural language based knowledge discovery tools

We consider linguistic data(base) summaries in the sense of Yager [Information Sciences 28 (1982) 69-86], exemplified by “most employees are young and well paid” (with some degree of truth added), for a personnel database, as an intuitive, human consistent and natural language based knowledge discovery tool. We present first an extension of the classic Yager’s approach to involve more sophisticated criteria of goodness, search methods, etc. We advocate the use of the concept of a protoform (prototypical form), that is recently vividly advocated by Zadeh [A prototype-centered approach to adding deduction capabilities to search engines—the concept of a protoform. BISC Seminar, University of California, Berkeley, 2002], as a general form of a linguistic data summary. We present an extension of our interactive approach, based on fuzzy logic and fuzzy database queries, which makes it possible to implement such linguistic data summaries. We show how fuzzy queries are related to linguistic summaries, and show that one can introduce a hierarchy of protoforms, or abstract summaries in the sense of latest Zadeh’s [A prototype-centered approach to adding deduction capabilities to search engines—the concept of a protoform. BISC Seminar, University of California, Berkeley, 2002] ideas meant mainly for increasing deduction capabilities of search engines. For illustration we show an implementation for a sales database in a computer retailer, employing some type of a protoform of a linguistic summary.     

基于模糊聚类的WRFM导购方法研究

提出了一种通过模糊聚类的方法将项目属性特征的相似性与项目属性权重相结合的推荐方法,首先应用AHP方法计算项目属性权重,其次运用模糊聚类技术对项目聚类,得到项目在属性特征上的相似关系群,最后,利用相似关系群综合给出推荐项目集.该方法利用Pareto的"80/20法则",在一定程度上实现了差异化对待客户,可有效改善基于项目的协同过滤推荐算法面临的对不同信誉度用户不能给出合理推荐问题,而且提高了导购系统的导购精度,减少了人工参与.     

A decision support system for optimizing dynamic courier routing operations

In this paper, we propose a prototype of a decision support system (DSS) that integrates a hybrid neighborhood search algorithm to solve the offline and online routing problems arising in courier service. In the dynamic operational environment of courier service, new customer orders and order cancellations continually arrive over time and thus disrupt the optimal routing schedule that was originally designed. This calls for the real-time re-optimization of routes. As service level is sensitive to whether allowable service time intervals are wide or narrow, it is valuable to study how adjustable and flexible time windows influence the courier service efficiency in a dynamic environment. To capture these dynamic features, a dynamic vehicle routing problem (DVRP) that simultaneously considers new customer orders and order cancellations is investigated in this study. Meanwhile, fuzzy time windows are formulated in the DVRP model to quantify the service level and explore the service efficiency. To tackle the new problem, we propose a competitive hybrid neighborhood search heuristic for (re)optimizing the offline and online routes. Numerical computational experiments and the comparison with results from Lingo show that our algorithm is capable of re-optimizing dynamic problems effectively and accurately in a very short time. The proposed model and algorithms are able to enhance courier service level without further expense of a longer traveling distance or a larger number of couriers.     

Decision support for team staffing: An automated relational recommendation approach

Selecting individuals for teams is only rarely supported by IS. Existing systems only consider whether a person has the required technical skills and abilities for a job. Another important aspect is neglected — the match between the person and the team members in terms of interpersonal compatibility. We present a decision support system based on a relational recommendation approach for providing an automated pre-selection of candidates that fit best with future team members. The relational recommender contributes to theory by proposing an IS-supported relational approach to team staffing and to practice by offering time and cost savings for HR professionals.     

基于犹豫模糊语言术语集的正交模糊聚类算法

犹豫模糊语言术语集（Hesitance Fuzzy Linguistic Term Sets,HFLTSs）允许决策者们用几个可能的语言术语来评估一个属性.近来,采用HFLTSs来进行模糊聚类分析的问题越来越受关注.考虑到目前基于HFLTSs的模糊聚类算法还存在计算复杂度高的问题,提出了一种新的正交模糊聚类算法：首先计算样本之间的距离测度得到距离测度矩阵,接着计算其等价矩阵;然后确定置信水平值,通过置信水平值对等价矩阵进行切割;最后根据切割矩阵的列向量之间的正交关系来确定对应样本是否可以放在同一个类别,以此得到聚类结果.该算法步骤简单,计算复杂度低,并且适合于数据量大的模糊聚类问题.本文末尾将通过一个实例结合k-means聚类算法证明该算法的可行性和高效性.     

模糊层次神经网络的学习算法

该文基于模糊层次神经网络模型,提出了模糊层次神经网络的比例学习算法。模拟结果表明利用该算法训练的模糊层次神经网络具有较好的非逻辑归纳能力和特征抽取能力,并且学习速度也大大加快。