案例1：Drug Treatments: In this case, imagine that you are a medical researcher compiling data for a study. You have collected data about a set of patients, all of whom suffered from the same illness. During their course of treatment, each patient responded to one of five medications. Part of your job is to use data mining to find out which drug might be appropriate for a future patient with the same illness.
案例2：Modeling Customer Response: This case is based on a company that wants to achieve more profitable results in future marketing campaigns by matching the right offer to each customer. Specifically, this case identifies the characteristics of customers who are most likely to respond, based on previous promotions, and generates a mailing list based on the results.
案例3：Classifying Telecommunications Customers: Suppose a telecommunications provider has segmented its customer base by service usage patterns, categorizing the customers into four groups. If demographic data can be used to predict group membership, you can customize offers for individual prospective customers.
案例4：Telecommunications Churn: Suppose a telecommunications provider is concerned about the number of customers it is losing to competitors. If service usage data can be used to predict which customers are liable to transfer to another provider, offers can be customized to retain as many customers as possible. This example focuses on using usage data to predict customer loss (churn).
案例5：Forecasting Bandwidth Utilization: An analyst for a national broadband provider is required to produce forecasts of user subscriptions in order to predict utilization of bandwidth. Forecasts are needed for each of the local markets that make up the national subscriber base. This example will use time series modeling to produce forecasts for the next three months for a number of local markets.
预测带宽使用率(通讯业)：某全国宽带网络供货商的分析师需进行客户使用网络的预估，以便预测带宽的使用。全国网络的使用是全国各局域网络使用的加总，因此分析师需逐一对给个区域市场进行带宽使用的预测。此案例的目的是想利用数据挖掘中的时间序列模型(预测模型-简单时间序列(Simple Time Series))来预测每个区域市场下三个月的带宽使用量。
案例6：Forecasting Catalog Sales: A catalog company is interested in forecasting monthly sales of its men’s clothing line, based on their sales data for the last 10 years. This example takes a closer look at the two methods that are available when choosing a model yourself—exponential smoothing and ARIMA.
案例7：Making Offers to Customers: This example teaches you how to predict which offers are most appropriate for customers and the probability of the offers being accepted. These sorts of models are most beneficial in customer relationship management, such as marketing applications or call centers.
案例8：Predicting Loan Defaulters: Suppose a bank is concerned about the potential for loans not to be repaid. If previous loan default data can be used to predict which potential customers are liable to have problems repaying loans, these “bad risk” customers can either be declined a loan or offered alternative products.
案例9：Retail Sales Promotion: This example deals with data that describes retail product lines and the effects of promotion on sales. The goal of this example is to predict the effects of future sales promotions.
案例10：Condition Monitoring: This example concerns monitoring status information from a machine and the problem of recognizing and predicting fault states. The data consists of a number of concatenated series measured over time. Each record is a snapshot report on the machine.
案例11：Classifying Cell Samples: A medical researcher has obtained a dataset containing characteristics of a number of human cell samples extracted from patients who were believed to be at risk of developing cancer. Analysis of the original data showed that many of the characteristics differed significantly between benign and malignant samples. The researcher wants to develop a model to give an early indication of whether their samples might be benign or malignant.
案例12：Market Basket Analysis: This example deals with data describing the contents of supermarket baskets (that is, collections of items bought together) plus the associated personal data of the purchaser, which might be acquired through a loyalty card scheme. The goal is to discover groups of customers who buy similar products and can be characterized demographically, such as by age, income, and so on.: