• 客户信用等级影响因素 – 客户信用卡申请数据预处理 – 信用卡申请成功影响因素 • 信用卡客户信用等级影响因素 • 基于消费的信用等级影响因素 • 信用卡欺诈判断模型 – 基于Apriori算法的欺诈模型 – 基于判别的欺诈模型 – 基于分类算法的欺诈模型

• 目前经济型酒店行业竞争态势 • 用户相关数据准备 • 通过Python编程抓取评论 • 数据预处理 • 商务宾馆客户数据分析 – 酒店评分影响因素 – 酒店评分与酒店业绩的关系 – 酒店评分分析 – 客户情感分析 – 竞争分析 • 建议

复旦大学：《应用伦理学》学生调查报告（课题PPT）关于“挖掘技术哪家强现象”背后的思考

• Sequence alignment – Algorithm – Parallel • Identification and mining – microRNA – machine learning related works • Function prediction – miRNA disease relationship – crops yield related genes

Motivation: Why data mining? What is data mining? A Multi-Dimensional View of Data Mining What Kinds of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Kinds of Technologies Are Used? What Kinds of Applications Are Targeted? Major Issues in Data Mining A Brief History of Data Mining and Data Mining Society Summary

◼ Data Preprocessing: An Overview ◼ Data Quality ◼ Major Tasks in Data Preprocessing ◼ Data Cleaning ◼ Data Integration ◼ Data Reduction ◼ Data Transformation and Data Discretization ◼ Summary

◼ Basic Concepts ◼ Frequent Itemset Mining Methods ◼ Which Patterns Are Interesting?—Pattern Evaluation Methods ◼ Summary

◼ Classification: Basic Concepts ◼ Decision Tree Induction ◼ Bayes Classification Methods ◼ Rule-Based Classification ◼ Model Evaluation and Selection ◼ Summary

◼ Outlier and Outlier Analysis ◼ Outlier Detection Methods ◼ Statistical Approaches ◼ Proximity-Base Approaches ◼ Clustering-Base Approaches ◼ Classification Approaches ◼ Summary

6.1 机器学习概述 6.2 符号学习 6.3 神经网络学习 6.4 知识发现与数据挖掘