计算机专业毕业设计外文翻译2份-中英文对照.docx

毕业论文外文文献翻译2份目 录1 外文文献译文（1）11.1建立客户模型与业务数据:一个自动的方法基于模糊聚类和机器学习11.2摘要11.3介绍11.4提案22 外文文献原文（1）32.1 title32.2 Abstract32.3 Introduction32.4 Our Proposal53 外文文献译文（2）63.1客户的知识关系管理：整合知识管理和客户关系管理过程63.2摘要63.3介绍63.4文献综述73.5提出客户知识关系管理过程的概念模型84 外文文献原文（2）84.1 title84.2 Abstract84.3 Introduction94.4 Literature Review104.5 Proposed Customer Knowledge Relationship Management Process: A Conceptual Model11 171 外文文献译文（1）1.1建立客户模型与业务数据:一个自动的方法基于模糊聚类和机器学习1.2摘要数据挖掘(DM)是一门新兴学科,旨在从数据中提取知识使用几种技术。DM证明是有用的业务数据的描述客户和他们的交易以兆兆字节。在本文中,我们提出的方法建立客户模型(也说在文献资料)与业务数据。我们的方法是三步。在第一步中,我们使用模糊聚类分类的客户,即确定客户群。一个关键特性是,很多团体(或集群)自动计算从数据使用划分熵作为真实性的标准。在第二步中,我们进行降维旨在保持为每组只有客户的信息最丰富的属性。为此,我们定义了信息损失量化信息程度的一个属性。因此,作为结果,第二步,我们获得的消费者群每个描述由一种独特的属性集。在第三个和最后一步,我们使用摘要神经网络中获取有用的知识从这些组织。真实世界的数据集上的实验结果揭示了我们的方法的良好性能,应该模拟未来的研究。关键字：数据挖掘、客户利润、客户关系、模糊聚类、降维、倒传递类神经网路、信息原理1.3介绍如果可以探测和预测客户的行为改变，营销管理能够和客户发展长期和愉快的关系。在过去，研究人员通常使用应用统计调查研究客户的行为。最近，数据挖掘技术已经采用，这些技术的目标通过搜索数据库以获取隐式的，未知，和潜在有用信息，包括知识规则、约束和规律。数据挖掘，是数据库的中知识发现关键的一步，包括特定算法的模式提取的应用，像网络、市场、金融和银行业务这些领域已经有多方面成功的应用。目前，企业面临的挑战是一个不断发展的市场，客户的需求在不断地变化。因此，代替对待所有客户一样，企业可以只选择那些符合特定的盈利条件的客户，标准基于他们个人的需求或消费行为。这样，所发现的信息可以为市场做更精准的决策。因此，可以定义对客户概括的数据挖掘，是简单地作为的允许建立描述一组特定的习惯、态度和行为客户档案的技术。数据挖掘技术在客户分析中面临一些困难，大量的数据可用来创建用户模型，数据是否适当，数据噪音问题和和捕获人类不确定性行为的必要性。数据挖掘和机器学习技术能够处理大量的数据和不确定性。这些特征使这些技术实现客户模型的自动生成，提高决策效率。一些文献提出人工智能技术可以解决这个问题。事实上，许多工业应用程序为了发展客户分析已经使用贝叶斯网络、决策树、支持向量机、人工神经网络和关联规则技术。以后，我们将列出一些对于客户分析的研究活动，向新手读者介绍一些该领域的背景知识。如果想对现有方法深入学习，感兴趣的读者参考相关的专业文献。第十七文献中提出一个集成的数据挖掘模型和行为得分模型管理银行现有的信用卡客户。区分基于还款行为时效性、频率、货币行为和得分的预测因子的客户群，使用自组织映射方法。同样将银行的客户分为三种主要盈利的客户群，使用先验的关联规则挖掘不同客户群的功能属性。其他方法也在零售市场得到应用，因为在动态的零售市场观察客户行为的变化可以帮助管理者建立有效的宣传活动。第五文献中的模型，合并了客户行为变量，人口统计学变量和事务数据库统计客户的行为变化。为了挖掘变化模式，相似性和不可预测性两种扩展特征用来分析不同时期模式的相似度。关联规则挖掘首次发现客户行为模式。自从发现了关联规则，通过比较两组不同时期的数据的关联规则确定客户行为的变化。基于先前的学习，客户行为改变包括出现模式、增加模式、流失模式和不确定模式。第四十文献中另一个值得注意的案例，现在这个模式使用到其他的服务，目前提供给移动电信用户。使用要素分析，聚类和定量关联规则这些方法发现细分客户群采用的服务模式，从这些分析中，确定了三种类别的用户。第一类用户由新一代利用额外服务收费的用户组成，组要为了休闲和娱乐。年轻的一代比年长的一代更频繁地使用手机，他们趋向展示更高的各种不同的附加服务使用模式。第二类用户使用实际的附加服务，低价或免费的如“数据服务”和“通话服务”通过“来电显示请求服务”。最后一类用户没有明显的特征。这份研究使用关联规则发现在每个用户群，为不同用户组的移动服务市场提供战略指导。第29文献中模型挖掘客户行为以帮助经理们为公司提出更好的促销活动和其他相关决策。关联规则的关系数据库设计实现了挖掘系统帮助电子目录设计和促销策略设计。关联规则在相关的数据库的应用挖掘消费行为，以便生成零售业购物中心的交叉销售的电子目录设计和营销方案。在本文中，我们提出一种方法从业务数据中开发自动客户分析(模型)。它涉及到三个步骤，在第一步中，用模糊聚类方法分类的客户。模糊聚类算法关键的一步是决定划分聚类的组数，自动从数据使用分区熵作为一个有效措施。在第二步中，维(或属性数量)对于每个集群(或一组客户)减少选择只有信息最丰富的属性。选择是基于属性信息的损失；量计算运用信息熵的属性。因此，第二步的结果，我们获取几组的消费者群，他们每个人都被一个不同的组，每组的属性被认定为信息最丰富的。在第三步，最后一个步骤中，每个聚类减少训练，从反馈网络中提取有用的知识。因此，连接客户分析(或模型)我们获取一组反馈网络编码和分类未知潜在的客户。下文组织如下，第二章中我们详细描述我们的模型。第三章中将进行实验分析了，而最后一章是论文总结和对未来研究的发展方向。1.4提案我们的方法是三步，总结了图1。首先，我们使用模糊聚类算法细分客户群。该方法的一个重要特性是使用划分熵作为一个有效性度量自动计算聚类的数量。通过这种方式，最优的聚类数量是能够产生最低划分熵。第一步的结果，从消费习惯，,收入等相似性方面细分获取几组描述客户类别。第二步，继续降维保持聚类的相关属性。事实上，聚类中的属性不是所有都是相关的，有一些属性应该删除。重复步骤，利用决策树方法来决定一个属性是否重要，关于给定一组客户中使用定义信息丢失的阈值。第三，每个聚类被导入倒传递网络提取有用的信息。整个算法的结果，获取客户细分结果。这样，新一轮分类后以前未知的客户就变成非常容易的任务。接下来的部分，将详细描述方法中每一步。2.2 第二步，属性的选择自然，在相同的集群(同一群顾客)中，并不是所有的初始属性是有效，其中有些应该被丢弃。衡量一个属性传达的信息，在一个给定的聚类的值，使用基于该属性值的频率的熵。我们将使用图2中相同的聚类方法计算这个频率，但是只适用于一个数据集组成的属性值。在给定一组客户中一个属性可能值的数量无法准确计算，特别是可能会非常接近的数值属性值，但不是完全相同的。给定客户群的属性运用图2的聚类方法，获取一组嵌入属性值非常类似的聚类；因此相应的集群多次相似的值被视为一个单一值。在进一步深入学习之前，现在我们需要以下的注释。2 外文文献原文（1）2.1 titleBuilding customer models from business data: an automatic approach based on fuzzy clustering maching learning2.2 AbstractData mining (DM) is a new emerging discipline that aims to extract knowledge from data using several techniques. DM turned out to be useful in business where the data describing the customers and their transactions is in the order of terabytes. In this paper, we propose an approach for building customer models (said also proles in the literature) from business data. Our approach is three-step. In the rst step, we use fuzzy clustering to categorize customers, i.e., determine groups of customers. A key feature is that the number of groups (or clusters) is computed automatically from data using the partition entropy as a validity criteria. In the second step, we proceed to a dimensionality reduction which aims at keeping for each group of customers only the most informative attributes. For this, we dene the information loss to quantify the information degree of an attribute. Hence, and as a result to this second step, we obtain groups of customers each described by a distinct set of attributes. In the third and nal step, we use backpropagation neural networks to extract useful knowledge from these groups. Experimental results on real-world data sets reveal a good performance of our approach and should simulate future research.Keywords: Data mining; customer proling; customer relationships management (CRM); fuzzy clustering; dimensionality reduction; backpropagation networks; information theory.2.3 IntroductionMarketing managers can develop long-term and pleasant relationships with customers if they can detect and predict changes in their behavior. In the past, researchers generally used to apply statistical surveys to study customer behavior. Recently, data mining techniques have been adopted. These techniques aim to search through a database to obtain implicit, previously unknown, and potentially useful information including knowledge rules, constraints and regularities. Data mining, a key step in Knowledge Discovery in Databases (KDD), involves the application of specic algorithms for pattern extraction. Various successful applications have been reported in areas such as the web, marketing, nance and banking. Currently, businesses face the challenge of a constantly evolving market where customer needs are changing all the time. Hence, instead of targeting all customers equally, enterprises can select only those customers who meet certain protability criteria based on their individual needs or purchasing behaviors. As a result, the discovered information can be ascertained to support better decisionmaking in marketing. Consequently, one can dene data mining in customer proling simply as being the technology that allows building customer proles each describing the specic habits, attitudes and behavior of a group of customers. Some of the diculties faced by data mining techniques for customer proling are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for the automatic generation of customer models that simulate human decision-making. Several Articial Intelligence techniques have been proposed in the literature to address this problem. In fact, models using Bayesian networks, decision trees, support vector machines, articial neural networks, and association rules have been used in many industrial applications in order to develop customer proles. Hereafter, we will outline some of the research activities for customer proling to give the novice reader some background in the eld. For an exhaustive review of existing approaches, we refer the interested reader to the specialized literature. r the interested reader to the666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666666The model in Ref. 17 proposes an integrated data mining and behavioral scoring model to manage existing credit card customers in a bank. A self-organizing map was used to identify groups of customers based on repayment behavior and recency, frequency, monetary behavioral and scoring predictors. It also classied bank customers into three major protable groups of customers. The resulting groups of customers were then proled by customers feature attributes determined using an apriori association rule inducer.Other works are also developed in retail marketing because understanding changes in customer behavior in the dynamic retail market can help managers to establish eective promotion campaigns. The model in Ref. 5 integrates customer behavioral variables, demographic variables, and transaction database to establish a method of mining changes in customer behavior. For mining change patterns, two extended measures of similarity and unexpectedness are designed to analyze the degree of resemblance between patterns at dierent time periods. Customer behavior patterns are rst identied using association rule mining. Once the association rules are discovered, the changes in customer behavior are identied by comparing two sets of association rules generated from two data sets at dierent periods. Based on previous studies, changes in customer behavior include emerging patterns, added patterns, perished patterns, and unexpected patterns.Another work worthy of notice is that proposed in Ref. 40 which presents the patterns of use for additional services that are currently provided to mobile telecommunication subscribers. Factor analysis, clustering and quantitative association rules are used to nd the service adoption patterns of segmented groups. From the analysis, three categories of users are identied. The rst group consists of a new generation of customers who utilize chargeable additional services using the “direct button”, for leisure and entertainment. The younger generation use their mobile phones more frequently than the older generation, and tend to display higher usage patterns for a variety of additional services. The second group utilizes practical additional services that are low-priced or free such as “data service” and “phone-to-phone service” via “Caller ID request service”. The customers in the nal group are people who have no general usage characteristics. This study utilizes the association rules found in each cluster to provide strategic guidance to enhance the mobile service market of the corresponding group.The model in Ref. 29 mines customer behavior to assist managers in developing better promotion and other relevant policies for a rm. The association rules of the relational database design are implemented in the mining system which provides electronic catalog designs and promotional policies. The association rules from relational database design are utilized to mine consumer behavior in order to generate cross-selling proposals for an electronic catalog design and marketing for a retailing mall.In this paper, we propose an approach to develop automatically customer proles (said also models) from business data. It involves three steps. In the rst step, we use fuzzy clustering to categorize customers. A key feature of this fuzzy clustering model is that the number of groups is determined automatically from data using the partition entropy as a validity measure. In the second step, the dimension (or number of attributes) for each cluster (or group of customers) is reduced by selecting only the most informative attributes. Selection is based on the information loss of an attribute; a quantity computed using the entropy of the attribute, and that of the whole group (or cluster). Consequently, and as a result to this second step, we obtain a set of groups of customers each of them described by a distinct set of attributes judged as being the most informative. In the third and nal step of our approach, each reduced cluster is trained by a feedforward backpropagation network to extract useful knowledge. Hence, we obtain a set of backpropagation networks each encoding in its connections a customer prole (or model) and which could be used subsequently in classifying new and previously unknown customers. The rest of this paper is structured as follows. In Sec. 2, we detail our model. Section 3 presents experimental analysis; while the last section oers concluding remarks and shades light on future research.2.4 Our ProposalOur approach is three-step and is summarized in Fig. 1. First, we use fuzzy clustering to identify groups of customers. An important characteristic of our approach is that the number of clusters is computed automatically using the partition entropy as a validity measure. In this way, the optimal number of clusters is that producing the lowest partition entropy. As a result to this rst step, we obtain a set of groups each describing a customer category whose consuming habits, incomes, etc., are similar. Second, we proceed to a dimensionality reduction retaining only pertinent attributes for each cluster. In fact, within a group of customers (cluster), not all original attributes are pertinent (or informative) and some of them should be discarded. Here again, we developed an entropy-based approach to decide whether or not an attribute is important with regard to a given group of customers using a user-dened threshold for the information loss. Third, each cluster is fed into a backpropagation neural network in order to extract useful knowledge. In this way, and as a result to the whole approach, we obtain a set of customer proles each encoded within the connections of a backpropagation network. Hence, classication (categorization) of new, previously unknown customers becomes an easy task. In the following subsection, we will detail each step of our approach.2.2. Second step: Attribute selectionNaturally, within the same cluster (the same group of customers), not all original attributes are “informative” and some of them should be discarded. As a measure for the “information” conveyed by an attribute, we will use the entropy based on the frequency of the values taken by that attribute within a given cluster. In order to compute this frequency, we will use the same clustering approach in Fig. 2 but applied to a data set composed of the values taken by that attribute. This is motivated by the following. The number of possible values for an attribute within a given a group of customers could not be computed exactly especially for numerical attributes where values could be “very close”, but not exactly the same. By applying the clustering approach in Fig. 2 to a given attribute within a given group of customers, we obtain a set of clusters each embedding values for that attribute that are very similar; and therefore considered as being a single value occurring as many times as the size of the corresponding cluster. Before going into further details, now we need the following notation.3 外文文献译文（2）3.1客户的知识关系管理：整合知识管理和客户关系管理过程3.2摘要 市场的激烈的竞争和业务环境的快速变化，信息利用已经成为企业增强竞争优势的关键，知识管理(KM)和客户关系管理(CRM)过程是一个崭新的研究领域，但是，围绕它科学研究和相关文献仍然有限，另外，客户获取、保持、扩张过程中知识管理的作用提高客户满意仍然处于研究和报告水平。本论文的目的根据知识管理和客户关系管理不同的模型，结合知识管理和客户关系管理提供一个概念性的框架，这个框架称为客户知识关系管理。主要强调了概念为基础的客户知识的概念(了解客户，客户信息，客户知识)。因此，本文研究知识管理过程的发展（客户知识发现、处理、运用）。本文分析研究乔丹公司如何利用知识管理过程提高客户关系管理过程。根据该公司的数据采集，结果分析表明知识管理过程对客户关系管理过程产生了积极的影响。 关键词：知识获取、知识创造、知识应用；客户的获取、客户保留、客户挖掘。3.3介绍 由于客户知识革命的快速发展，建立同客户高效且有效的的关系非常需要知识管理过程。另外，客户关系管理的本质是知识管理，因为客户关系管理帮助企业加强服务，快速响应客户需求。企业需要加强与客户的互动，确定知识管理的相关活动领域，改善流程。 此外，知识管理是一个捕获、创建和应用知识使客户关系管理过程成功的方法。更进一步，Gebert提出客户关系管理和知识管理已经在商业市场中引起广泛的兴趣。这两种方法关注分配资源，支持业务活动，以获得竞争优势，尽管两个概念实际上作为分开的研究的领域。Lin认为客户关系管理和知识管理对每个市场决策者和信息技术专业人员有重要意义。 客户关系管理的知识管理是获得客户满意重要方法。知识管理过程和客户关系管理过程的结合一个新的研究领域，因此围绕它科学研究和相关文献仍然有限，知识管理过程对客户关系管理过程中的影响利用仍然处于研究和报告水平。本论文的目的在于提出客户知识关系管理概念模型，整合知识管理过程和客户关系管理过程提提提高客户满意度。将会实现下面的目的：1、 确定目前企业如何处理客户，通过分析企业的使命完成。2、 提高对客户知识的获取以获得新客户，通过利用客户知识保持现有客户，拓展客户知识扩大与客户的关系。3、 描述未来企业如何处理客户关系，识别企业的愿景实现目标。接下来部分，查阅相关文献，第三章提出客户知识关系管理过程模型，第四章提出研究方法。3.4文献综述 本节概述不同文献关于知识管理过程，提供了CRM过程的描述。最后，描述相关文献对知识管理过程和客户关系管理过程的关系。 2.1 知识管理过程 知识管理的目的不是处理所有知识，对企业来说管理知识是最重要的。它涉及应 用收集的知识和全部人力物力实现企业特定的目标，合适的人利用合适的信息在合适的时间帮助人们收集和分析知识提高企业效益。作者提出并发展了一个概念和知识管理清晰模型。基于知识管理文献中多种模型的调查，从知识的过程中捕获知识，知识处理中产生所需的知识，知识提取中应用知识。根据下面表1中知识管理过程的分类法。 2.2客户关系管理过程 客户关系管理是近年来相关领域中出现的最热门的课题，因为企业客户关系管理的价值。更进一步，客户关系管理成为所有企业的重要的业务流程和任务。作者提出对客户关系管理的清晰模型概念化，如下图表2依靠CRM流程的分类法，客户流程获取新客户，客户流程保持客户，流程扩大与客户的关系。 2.3知识管理和客户关系管理流程之间的关系 客户关系管理中的知识管理很重要，因为这帮助企业做更好的服务，加强产品的质量，减少费用和迅速响应客户。然而，企业管理知识最突出的挑战是在所有不同部门成员之间计算和整合知识。因此，知识管理是成功的客户关系管理战略关键因素之一，提高服务质量，降低服务成本，发展新产品和服务客户。只有少数的企业实现把信息转成客户知识。 另外，Salomann区分三种知识流动在企业和客户交互中的起到至关重要的作用：客户知识在他们的购买周期间支持客户；一个连续的知识流直接从公司向它的客户。来自客户的知识的必须合并公司产品和服务的创新和发展。关于客户知识的收集通过客户关系管理服务，支持流程和客户关系管理分析过程。 Ocker 和 Mudambi指出这些企业需要探索和改进客户关系管理和知识管理的方法为企业和客户增加额外的知识价值。意识到以客户为中心的知识是公司客户数据和知识的整合。另外，Geib对客户关系管理的定义客户满意管理通过向客户提供高质量的服务和互动提供高价值客户的满意度。为了得到更好的服务质量和提供流程和问题的解决方案，这些目标往往由知识管理系统实现。 根据以上研究，构建一个高效的和有效的客户关系，知识管理过程是关键。客户关系管理中的知识管理应用对提高客户满意度起重要的作用。深入探索所有可用资源中所有可用的研究，建议，客户关系管理过程整合知识管理过程仍需要更进一步的研究。因此，企业意识到客户关系管理的成功知识管理起重要的作用，企业需要整合他们的知识管理流程和客户关系管理流程。3.5提出客户知识关系管理过程的概念模型 本节对知识管理和客户关系管理流程整合的客户知识关系管理提出一个概念模型，目的是提高客户满意度。根据Alryalat提出的知识管理流程和客户关系管理流程，模型1解释了各种形式的客户知识的流向（关于客户的知识，获取客户知识，挖掘客户知识）和知识管理过程（ 客户知识管理过程的说明，客户知识管理过程的目的，客户知识管理过程的利用）。 客户知识关系管理模型中有12阶段。第一个阶段确定企业的目标，明确目前企业和客户的状态。这个阶段的目的是阐明发展客户关系的优缺点，分析企业与客户目前的关联，分析企业为客户提供的服务。这阶段的目的是观察目前的客户关系是否符合企业的目标。 第二阶段是关于客户知识的流程，这个阶段对获取客户非常重要、必不可少。这个阶段的主要目标理解如何捕获所需知识。关于客户的知识流程需要每个阶段的序列。 第一个阶段是需要客户知识。需要许多人力物力在任何地方和任何时间去收集信息，知识的利用对提高工作效率节约时间和成本显示巨大优势。另外，Sunassee 和 Sewry认为创建企业需要的知识要选择企业内部和外部的知识。同样，区别知识的要充分了解需要知识的特点，挑选目前相关的知识和分配要获取和创建的知识资源。此外，通过理解和选择现有的存储库中有用的知识，知识选择有助于识别知识的需求。这一过程能更容易搜索和发现知识。知识的识别包括识别和确定需要知识，在知识可以创建或共享前，对知识的需求已经确认。4 外文文献原文（2）4.1 titleTowards Customer Knowledge Relationship Management:Integrating Knowledge Management and Customer Relationship Management Process4.2 AbstractDue to the strong competition that exists among organisations and the rapid change in the business environment, knowledge has turned out to become a key source for organisations to enhance the competitive advantage. Integrating Knowledge Management (KM) and Customer Relationship Management (CRM) process is a new research area, therefore, scientic research and literature around it remain limited. In addition, the impact of KM process on customer acquisition, retention, and expansion to improve customer satisfaction remains under study and report. The aim of this paper is to presen