《数据挖掘应用.pptx》由会员分享，可在线阅读，更多相关《数据挖掘应用.pptx（44页珍藏版）》请在taowenge.com淘文阁网|工程机械CAD图纸|机械工程制图|CAD装配图下载|SolidWorks_CaTia_CAD_UG_PROE_设计图分享下载上搜索。
1、CRM第1页/共44页顾客生命周期寿命盈利 获取消费者保持消费者消费者分析和恢复收入支出寿命第2页/共44页数据挖掘在数据挖掘在CRMCRM中的应用中的应用第3页/共44页Customer identificationCustomer identificationCRM begins with customer identification.This phase involves targeting the population who are most likely to become customers or most profitable to the company.It also i
2、nvolves analyzing customers who are being lost to the competition and how they can be won back.Elements for customer identification include target customer analysis and customer segmentation.第4页/共44页Customer attractionCustomer attractionOrganizations can direct effort and resources into attracting t
3、he target customer segments.Direct marketing is a promotion process which motivates customers to place orders through various channels.direct mail or coupon第5页/共44页目标营销目标营销第6页/共44页Customer retentionCustomer retentionCentral concern for CRM.Customer satisfaction is the essential condition for retaini
4、ng customers.Elements of customer retention include one-to-one marketing,loyalty programs and complaints management.One-to-one marketing refers to personalized marketing campaigns which are supported by analyzing,detecting and predicting changes in customer behaviors.Loyalty programs involve campaig
5、ns or supporting activities which aim at maintaining a long term relationship with customers.Churn analysis,credit scoring,service quality or satisfaction form part of loyalty programs.第7页/共44页客户流失分析第8页/共44页Customer developmentCustomer developmentElements of customer development include customer lif
6、etime value analysis,up/cross selling and market basket analysis.Customer lifetime value analysis is defined as the prediction of the total net income a company can expect from a customer.Up/Cross selling refers to promotion activities which aim at augmenting the number of associated or closely rela
7、ted services that a customer uses within a firm.Market basket analysis aims at maximizing the customer transaction intensity and value by revealing regularities in the purchase behaviour of customers.第9页/共44页Personalized recommendation systems 第10页/共44页Personalized recommendationPersonalized recomme
8、ndationPersonalization is defined as“the ability to provide content and services tailored to individuals based on knowledge about their preferences and behavior”or“the use of technology and customer information to tailor electronic commerce interactions between a business and each individual custome
9、r”Internet recommendation systems(Internet recommender systems)in electronic commerce is to reduce irrelevant content and provide users with more pertinent information or product.A recommendation system is a computer-based system that uses profiles built from past usage behavior to provide relevant
10、recommendations.第11页/共44页Information filtering and recommendationrule-based filtering,content-based filtering,and collaborative filtering.Rule-based filtering uses pre-specified if-then rules to select relevant information for recommendation.Content-based filtering uses keywords or other product-rel
11、ated attributes to make recommendations.Collaborative filtering uses preferences of similar users in the same reference group as a basis for recommendation.第12页/共44页Typical personalization processunderstanding customers through profile buildingdelivering personalized offering based on the knowledge
12、about the product and the customer measuring personalization impact第13页/共44页Inadequate information in IROne possible solution for overcoming the problem is to expand the query by adding more semantic information to better describe the concepts.Relevance feedbacks and knowledge structure are used to
13、add appropriate terms to expand the queries.Relevance feedbacks are information on the items selected by the user from the output of previous queries.第14页/共44页Spreading Activation ModelIn the Spreading Activation(SA)Model,concepts are expanded based on the semantics in the process of identifying cus
14、tomer profile and matching items and the model has been applied to expand queries.第15页/共44页A personalized knowledge recommendation system A semantic-expansion approach to build the user profile by analyzing documents previously read by the person.The semantic-expansion approach that integrates seman
15、tic information for spreading expansion and content-based filtering for document recommendation.第16页/共44页A sample semantic-expansion network第17页/共44页Experimental resultsAn empirical study using master theses in the National Central library in Taiwan shows that the semantic-expansion approach outperf
16、orms the traditional keyword approach in catching user interests.第18页/共44页构件库管理第19页/共44页自适应构件检索自适应构件检索构件检索是构件库研究中的重要问题，有效的构件检索机制能够降低构件复用成本。构件的复用者并不是构件的设计者或构件库的管理员，在检索构件时对构件库的描述理解不充分，导致难以给出完整和精确的检索需求。用户选择构件的结果反映其真实需求，如果能够从用户的检索行为以及用户对检索结果的反馈中推断出用户的非精确检索条件与用户实际需要的精确检索条件之间内在联系的模式，就可以提高系统的查准率。第20页/共44页基
17、于关联挖掘的自适应构件检基于关联挖掘的自适应构件检索索把关联规则挖掘方法引入构件检索，从用户检索行为以及反馈中挖掘出非精确检索条件与精确检索结果之间的关联规则，从而调整检索机制，提高构件检索的查准率。第21页/共44页实例实例windows windows,SQL ServerLinux Linux,Mysql金融 金融,SQL Serverwindows,金融 windows,金融,SQL Server第22页/共44页供应链管理第23页/共44页零部件供应商选择零部件供应商选择如何选择供应商不仅决定了产品的质量和成本,也决定了产品的销售价格、维护费用和用户满意程度。选择供应商一般以满足时间
18、约束的条件下最小化物流成本为目标，没有考虑零部件故障率与不同地域环境之间的相关性。第24页/共44页基于关联规则的零部件供应商选择基于关联规则的零部件供应商选择使用关联规则挖掘算法，从产品维修记录中，寻找不同供应商提供的产品零部件及其组合在不同地域的频繁故障模式。在生成供应商选择和配送方案过程中，利用这些频繁故障模式，选择合适的零部件供应商组合，达到物流成本与产品维护成本的联合优化。第25页/共44页人力资源管理第26页/共44页人力资源管理人力资源管理人力资源在高科技公司中的地位相当重要。人力招聘直接影响公司员工的素质，但传统的人力资源管理方法已经不适应高科技公司的需要。高科技行业知识不断变
19、化，工作不易定界，跨职能任务较多，工作过程趋于多元化。这些因素都对员工素质提出了更高的要求，依靠传统方法获知竞聘者是否能够胜任工作变得比较困难。第27页/共44页采用决策树挖掘出人员选拔规采用决策树挖掘出人员选拔规则则CHAID第28页/共44页Decision tree for predicting job performance第29页/共44页Improving education第30页/共44页Improving teaching and learningInstructors can have trouble identifying their real difficulties
20、in learning.Based on the students testing records,the system works to identify and find those problems,and then comes up with its suggestions for designing new teaching strategies.Assist teachers to identify students specific difficulties and weaknesses in learning.Helps the student to find out his
21、or her weak points in learning and offers improvement recommendations.第31页/共44页ESL recommender teaching and learning第32页/共44页Right/wrong answer statistical tableFor every student,the system creates a right/wrong answer statistical table:a wrong answer is represented by 1 and a right answer by 0.第33页
22、/共44页Summary table of students wrong answers The right/wrong answer statistical tables for respectivestudents are integrated in a summary tableof students wrong answers,and the sum values in the table are then ranked in descending order so as to show the descending degrees of weaknesses the students
23、 have collectively.第34页/共44页Hierarchical clusteringHierarchical clustering algorithm is then applied to data collected to segment the students into acertain number of clusters,or categories,each of whichincludes students sharing the same or similar characteristics.第35页/共44页All students right/wrong a
24、nswer statistical tables第36页/共44页Clustering analysisA clustering analysis is made of the data in All students right/wrong answer statistical tables.It is evident that the students whose numbers are enclosed in the following separate parentheses belong to different clusters respectively:(9,15,6,17,13
25、,19,14,5);(22,23,4,3,21,11,24,20,7,1);(12,18,2,8,25,10,16).第37页/共44页搜索引擎优化搜索引擎优化第38页/共44页第39页/共44页搜索引擎优化They are usually not search engines by themselves.The clustering engine uses one or more traditional search engines to gather a number of results;then,it does a form of post-processing on these re
26、sults in order to cluster them into meaningful groups.The post-processing step analyzes snippets,i.e.,short document abstracts returned by the search engine,usually containing words around query term occurrences.第40页/共44页研讨题阅读后面参考文献，分析案例使用的数据挖掘方法以及解决的主要问题。结合自己的实践，说明所在岗位对商务智能的需求（针对软件工程硕士）。第41页/共44页典型
27、参考文献(1)Chen-Fu Chien,Li-Fei Chen.Data mining to improve personnel selection and enhance human capital:a case study in high-technology industry.Expert Systems with Application,2008,(34):280-290Cristobal Romero,Sebastian Ventura,Enrique Garca.Data mining in course management systems:Moodle case study
28、and tutorial.Computers&Education 51(2008)368384Yang,C.C.et al.,Improving scheduling of emergency physicians using data mining analysis,Expert Systems with Applications(2008),doi:10.1016/j.eswa.2008.02.069Jang Hee Lee,Sang Chan Park.Intelligent profitable customers segmentation system based on busine
29、ss intelligence tools.Expert Systems with Applications 29(2005):145152Chih-Ming Chen,Ying-Ling Hsieh,Shih-Hsun Hsu.Mining learner profile utilizing association rule for web-based learning diagnosis.Expert Systems with Applications 33(2007)622Bong-Horng Vhu,Ming-Shian Tsai,Cheng-Seen Ho.Toward a hybr
30、id data mining model for custer retention.Knowledge-Based Systems 20(2007)703718第42页/共44页Daniela Grigoria,Fabio Casatib,Malu Castellanos,et al.Business process intelligence.Computers in Industry 53(2004)321343Dursun Delen,Christie Fuller,Charles McCann.Analysis of healthcare coverage:A data mining a
31、pproach.Delen,D.et al.,Analysis of healthcare coverage:A data mining approach,Expert Systems with Applications(2007),doi:10.1016/j.eswa.2007.10.041Mei-Hua Hsu.Proposing an ESL recommender teaching and learning system.Expert Systems with Applications.2008,34:21022110Yi-Fan Wang,Ding-An Chiang,Mei-Hua
32、 Hsu,et al.A recommender system to avoid customer churn:A case study.Expert Systems with Applications,2009,36:80718075倪日文,徐晓飞,邓胜春.基于关联规则的零部件供应商选择优化.计算机集成制造系统，2004,10(3):317-335薛云皎,钱乐秋,花鸣等.一种基于关联挖掘的自适应构件检索方法.电子学报，2004,32(12A):203-206Ting-Peng Liang,Yung-Fang Yang,Deng-Neng Chen,et al.A semantic-expansion approach to personalized knowledge recommendation.Decision Support Systems,2008,(45):401-412Giansalvatore Mecca,Salvatore Raunich,Alessandro Pappalardo.A new algorithm for clustering search results.Data&Knowledge Engineering 62(2007)504522典型参考文献(2)第43页/共44页谢谢您的观看！第44页/共44页