《数据挖掘应用》PPT课件.ppt

1、数据挖掘应用,CRM,顾客生命周期,数据挖掘在CRM中的应用,Customer identification,CRM 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 involves analyzing customers who are being lost to the competition and how t

2、hey can be won back. Elements for customer identification include target customer analysis and customer segmentation.,Customer attraction,Organizations can direct effort and resources into attracting the target customer segments. Direct marketing is a promotion process which motivates customers to p

3、lace orders through various channels. direct mail or coupon,目标营销,Customer retention,Central concern for CRM. Customer satisfaction is the essential condition for retaining customers. Elements of customer retention include one-to-one marketing, loyalty programs and complaints management. One-to-one m

4、arketing refers to personalized marketing campaigns which are supported by analyzing, detecting and predicting changes in customer behaviors. Loyalty programs involve campaigns or supporting activities which aim at maintaining a long term relationship with customers. Churn analysis, credit scoring,

5、service quality or satisfaction form part of loyalty programs.,客户流失分析,Customer development,Elements of customer development include customer lifetime value analysis, up/cross selling and market basket analysis. Customer lifetime value analysis is defined as the prediction of the total net income a c

6、ompany can expect from a customer. Up/Cross selling refers to promotion activities which aim at augmenting the number of associated or closely related services that a customer uses within a firm. Market basket analysis aims at maximizing the customer transaction intensity and value by revealing regu

7、larities in the purchase behaviour of customers.,Personalized recommendation systems,Personalized recommendation,Personalization 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 c

8、ustomer information to tailor electronic commerce interactions between a business and each individual customer” Internet recommendation systems (Internet recommender systems) in electronic commerce is to reduce irrelevant content and provide users with more pertinent information or product. A recomm

9、endation system is a computer-based system that uses profiles built from past usage behavior to provide relevant recommendations.,Information filtering and recommendation,rule-based filtering, content-based filtering, and collaborative filtering. Rule-based filtering uses pre-specified if-then rules

10、 to select relevant information for recommendation. Content-based filtering uses keywords or other product-related attributes to make recommendations. Collaborative filtering uses preferences of similar users in the same reference group as a basis for recommendation.,Typical personalization process,

11、understanding customers through profile building delivering personalized offering based on the knowledge about the product and the customer measuring personalization impact,Inadequate information in IR,One possible solution for overcoming the problem is to expand the query by adding more semantic in

12、formation to better describe the concepts. Relevance feedbacks and knowledge structure are used to add appropriate terms to expand the queries. Relevance feedbacks are information on the items selected by the user from the output of previous queries.,Spreading Activation Model,In the Spreading Activ

13、ation (SA) Model, concepts are expanded based on the semantics in the process of identifying customer profile and matching items and the model has been applied to expand queries.,A personalized knowledge recommendation system,A semantic-expansion approach to build the user profile by analyzing docum

14、ents previously read by the person. The semantic-expansion approach that integrates semantic information for spreading expansion and content-based filtering for document recommendation.,A sample semantic-expansion network,Experimental results,An empirical study using master theses in the National Ce

15、ntral library in Taiwan shows that the semantic-expansion approach outperforms the traditional keyword approach in catching user interests.,构件库管理,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

16、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。,基于关联挖掘的自适应构件检索,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。,实例,windows windows ,SQL Server Linux Linux ,Mysql 金融 金融,SQL Server windows ,金融 windows ,金融,SQL Server,供应链管理,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

17、应商一般以满足时间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。,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。,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

18、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。,采用决策树挖掘出人员选拔规则,CHAID,Decision tree for predicting job performance,Improving education,Improving teaching and learning,Instructors can have trouble identifying their real difficulties in learning. Based on the students testing records, the system works to iden

19、tify 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 or her weak points in learning and offers improvement recommendations.,

20、ESL recommender teaching and learning,Right/wrong answer statistical table,For every student, the system creates a right/wrong answer statistical table: a wrong answer is represented by 1 and a right answer by 0.,Summary table of students wrong answers,The right/wrong answer statistical tables for r

21、espective students are integrated in a summary table of 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 have collectively .,Hierarchical clustering,Hierarchical clustering algorithm is then ap

22、plied to data collected to segment the students into a certain number of clusters, or categories, each of which includes students sharing the same or similar characteristics.,All students right/wrong answer statistical tables,Clustering analysis,A clustering analysis is made of the data in All stude

23、nts 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, 19, 14, 5); (22, 23, 4, 3, 21, 11, 24, 20, 7, 1);(12, 18, 2, 8, 25, 10, 16).,搜索引擎优化,搜索引擎优化,They are

24、 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 results in order to cluster them into meaningful groups. The post-processing step analyzes snippets, i.e., shor

25、t document abstracts returned by the search engine, usually containing words around query term occurrences.,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）。,典型参考文献(1),Chen-Fu Chien, Li-Fei Chen. Data mining to improve personnel selection and enhance human capital: a case study

26、in high-technology industry. Expert Systems with Application, 2008,(34):280-290 Cristobal Romero, Sebastian Ventura, Enrique Garca. Data mining in course management systems: Moodle case study and tutorial. Computers & Education 51 (2008) 368384 Yang, C. C. et al., Improving scheduling of emergency p

27、hysicians using data mining analysis, Expert Systems with Applications (2008), doi:10.1016/j.eswa.2008.02.069 Jang Hee Lee, Sang Chan Park. Intelligent profitable customers segmentation system based on business intelligence tools. Expert Systems with Applications 29 (2005): 145152 Chih-Ming Chen, Yi

28、ng-Ling Hsieh, Shih-Hsun Hsu. Mining learner profile utilizing association rule for web-based learning diagnosis. Expert Systems with Applications 33 (2007) 622 Bong-Horng Vhu, Ming-Shian Tsai, Cheng-Seen Ho. Toward a hybrid data mining model for custer retention. Knowledge-Based Systems 20 (2007) 703718,Daniela Grigoria, Fabio Casatib, Malu Castellanos, et al. Business process intelligence. Computers in Industry 53 (2004) 321343 Dursun Delen, Christie Fuller, Ch