tailieunhanh - Ebook Business analytics (2nd edition): Part 2

(BQ) Part 2 book "Business analytics" hass contents: Introduction to data mining, spreadsheet modeling and analysis, monte carlo simulation and risk analysis, linear optimization, applications of linear optimization, integer optimization, decision analysis. | Chapter 10 Introduction to Data Mining kensoh/ Learning Objectives After studying this chapter, you will be able to: • Define data mining and some common approaches used in data mining. • Explain how cluster analysis is used to explore and reduce data. • Apply cluster analysis techniques using XLMiner. • Explain the purpose of classification methods, how to measure classification performance, and the use of training and validation data. • Apply k-Nearest Neighbors, discriminant analysis, and logistic regression for classification using XLMiner. Describe association rule mining and its use in market basket analysis. Use XLMiner to develop association rules. Use correlation analysis for cause-and-effect modeling • • • 327 328 Chapter 10  Introduction to Data Mining In an article in Analytics magazine, Talha Omer observed that using a cell phone to make a voice call leaves behind a significant amount of data. “The cell phone provider knows every person you called, how long you talked, what time you called and whether your call was successful or if was dropped. It also knows where you are, where you make most of your calls from, which promotion you are responding to, how many times you have bought before, and so on.”1 Considering the fact that the vast majority of people today use cell phones, a huge amount of data about consumer behavior is available. Similarly, many stores now use loyalty cards. At supermarket, drugstores, retail stores, and other outlets, loyalty cards enable consumers to take advantage of sale prices available only to those who use the card. However, when they do, the cards leave behind a digital trail of data about purchasing patterns. How can a business exploit these data? If they can better understand patterns and hidden relationships in the data, they can not only understand buying habits but also customize advertisements, promotions, coupons, and so on, for each individual customer and send targeted text .