Data mining is regularly used in Applications 甲 Market segmen tation Risk Analysis e Affin ity Analys is e Profiling w Portfolio selection ● Direct Mai Market Bas ket Analysis Credit Approva CRM Defect Analysis a Fraud Detection Events analy sis P Fraud patterns Forecasting Acqu is ition a Profitabilit detection Data Mining Techniques Lin k Analysis Frequency Analysis Clustering C lassification Prediction Algorithms Associations sequential Similar Dem og raphic Neural Decision Radial basis Patterns sequences Clustering Networks Trees Functions
Data mining is regularly used in
典型的数据挖掘系统结构 图形用户界面 模式评估 数据挖掘 知识库 引 数据库或 数据仓库服务器 数据清理数据集成 过滤 数据库 数据仓库
典型的数据挖掘系统结构
Verification-Driven Analysis Verification-driven data mining tools extract data The user is expected to generate information based on his interpretation of the returned data supp。rt Verification Generation of of Hypotheses Hypothes Verification Mode SQL, OLAP Data Mining,… Query and Data Data Analysis Mining Know unknown Correlations Correlations rack Verify Discover Answer Analyze
Verification-Driven Analysis ◼ Verification-driven data mining tools extract data. The user is expected to generate information based on his interpretation of the returned data
New Process With Data Mining Discovery-driven Computer sifts through millions of hypotheses and only presents the most interesting/valid ones Example From a sample group of clients that have defected to a competitive bank-identify client characteristics that are strongly correlated and using these attributes score the rest of the client and prospect population and the strength of their relationships to sample group
New Process With Data Mining ◼ Discovery-driven ◼ Computer sifts through millions of hypotheses and only presents the most interesting/valid ones ◼ Example: ◼ From a sample group of clients that have defected to a competitive bank - identify client characteristics that are strongly correlated, and using these attributes, score the rest of the client and prospect population and the strength of their relationships to sample group
数据挖掘可以做什么? 分类和预测 Grouping and Card 聚类 Market Based Analysis and Fraud ■关联分析 SS- Selling/cross 描述和可视化 Determinatio Pharmaceutical Patient Type Turnover Predictions Defect Analysis University and Recruitment
数据挖掘可以做什么? ◼ 分类和预测 ◼ 聚类 ◼ 关联分析 ◼ 描述和可视化 Market Based Analysis and UpSelling/CrossSelling Pharmaceutical Industry: Drug Effectiveness by Patient Type Defect Analysis in Manufacturing University and Employee Recruitment Employee Turnover Predictions Credit Risk Determination Credit Card Fraud Customer Grouping and Behaviour Prediction