3. What do you think is the most prominent application area for data mining? Why? Students answers will differ depending on which of the applications(most likely banking, retailing and logistics, manufacturing and production, government, healthcare, medicine, or homeland security) they think is most in need of greater certainty. Their reasons for selection should relate to the application areas need for better certainty and the ability to pay for the investments in data mining Can you think of other application areas for data mining not discussed in this section? Explain Students should be able to identify an area that can benefit from greater prediction or certainty. Answers will vary depend ing on their creativit Section 4.4 Review Questions What are the major data mining processes Similar to other information systems initiatives, a data mining project must follow a systematic project management process to be successful. Several data mining processes have been proposed: CRISP-DM, SEMMA, and KDD 2. Why do you think the early phases(understanding of the business and understand ing of the data) take the longest in data mining projects? Students should explain that the early steps are the most unstructured phases because they involve learning. Those phases(learning/understanding) cannot be automated. Extra time and effort are needed upfront because any mistake in understand ing the business or data will most likely result in a failed BI project 3. List and briefly define the phases in the CriSP-dM proce CRISP-DM provides a systematic and orderly way to conduct data mining projects. This process has six steps. First, an understanding of the data and an understand ing of the business issues to be addressed are developed concurrently Next, data are prepared for modeling, are modeled; model results are evaluated and the models can be employed for regular use What are the main data preprocessing steps? Briefly describe each step and provide relevant examples Data preprocessing is essential to any successful data mining study. Good data leads to good information; good information leads to good decisions. Data preprocessing includes four main steps(listed in Table 4. 1 on page 167) data consolidation: access. collect. select and filter data 6 Copyright C2018 Pearson Education, Inc.6 Copyright © 2018Pearson Education, Inc. 3. What do you think is the most prominent application area for data mining? Why? Students’ answers will differ depending on which of the applications (most likely banking, retailing and logistics, manufacturing and production, government, healthcare, medicine, or homeland security) they think is most in need of greater certainty. Their reasons for selection should relate to the application area’s need for better certainty and the ability to pay for the investments in data mining. 4. Can you think of other application areas for data mining not discussed in this section? Explain. Students should be able to identify an area that can benefit from greater prediction or certainty. Answers will vary depending on their creativity. Section 4.4 Review Questions 1. What are the major data mining processes? Similar to other information systems initiatives, a data mining project must follow a systematic project management process to be successful. Several data mining processes have been proposed: CRISP-DM, SEMMA, and KDD. 2. Why do you think the early phases (understanding of the business and understanding of the data) take the longest in data mining projects? Students should explain that the early steps are the most unstructured phases because they involve learning. Those phases (learning/understanding) cannot be automated. Extra time and effort are needed upfront because any mistake in understanding the business or data will most likely result in a failed BI project. 3. List and briefly define the phases in the CRISP-DM process. CRISP-DM provides a systematic and orderly way to conduct data mining projects. This process has six steps. First, an understanding of the data and an understanding of the business issues to be addressed are developed concurrently. Next, data are prepared for modeling; are modeled; model results are evaluated; and the models can be employed for regular use. 4. What are the main data preprocessing steps? Briefly describe each step and provide relevant examples. Data preprocessing is essential to any successful data mining study. Good data leads to good information; good information leads to good decisions. Data preprocessing includes four main steps (listed in Table 4.1 on page 167): data consolidation: access, collect, select and filter data