《商务智能：数据分析的管理视角 Business Intelligence, Analytics, and Data Science：A Managerial Perspective》教学资源（PPT课件，第3版）Chapter 04 Data Mining

Business Intelligence: A Managerial Perspective on Analytics(3rd Edition) INTELLIGENCE A Managerial Perspective on Analytics Chapter 4: EFRAUTI RRAN Data Mining

Chapter 4: Data Mining Business Intelligence: A Managerial Perspective on Analytics (3rd Edition)

Learning Objectives Define data mining as an enabling technology for business intelligence Understand the objectives and benefits of business analytics and data mining Recognize the wide range of applications of data mining Learn the standardized data mining processes CRISP-DM SEMMA KDD Continued.) Copynight@ 2014 Pearson Education, Inc Slide 4-2

Copyright © 2014 Pearson Education, Inc. Slide 4- 2 Learning Objectives ▪ Define data mining as an enabling technology for business intelligence ▪ Understand the objectives and benefits of business analytics and data mining ▪ Recognize the wide range of applications of data mining ▪ Learn the standardized data mining processes ▪ CRISP-DM ▪ SEMMA ▪ KDD (Continued…)

Learning Objectives Understand the steps involved in data preprocessing for data mining Learn different methods and algorithms of data mIning Build awareness of the existing data mining software tools Commercial versus free/open source Understand the pitfalls and myths of data mining Copynight@ 2014 Pearson Education, Inc Slide 4-3

Copyright © 2014 Pearson Education, Inc. Slide 4- 3 Learning Objectives ▪ Understand the steps involved in data preprocessing for data mining ▪ Learn different methods and algorithms of data mining ▪ Build awareness of the existing data mining software tools ▪ Commercial versus free/open source ▪ Understand the pitfalls and myths of data mining

Opening Vignette Cabela's reels in more Customers with Advanced Analytics and Data Mining Decision situation Problem Proposed solution Results Answer discuss the case questions Copynight@ 2014 Pearson Education, Inc Slide 4-4

Copyright © 2014 Pearson Education, Inc. Slide 4- 4 Opening Vignette… Cabela’s Reels in More Customers with Advanced Analytics and Data Mining ▪ Decision situation ▪ Problem ▪ Proposed solution ▪ Results ▪ Answer & discuss the case questions

Questions for the Opening Vignette 1. Why should retailers, especially omni-channel retailers pay extra attention to advanced analytics and data mining? 2. What are the top challenges for multi-channel retailers? Can you think of other industry segments that face similar problems/challenges? 3. What are the sources of data that retailers such as Cabela's use for their data mining projects? 4. What does it mean to have a single view of the customer"? How can it be accomplished? 5. What type of analytics help did Cabela's get from their efforts? Can you think of any other potential benefits of analytics for large-scale retailers like Cabela's? Copynight@ 2014 Pearson Education, Inc Slide 4-5

Copyright © 2014 Pearson Education, Inc. Slide 4- 5 Questions for the Opening Vignette 1. Why should retailers, especially omni-channel retailers, pay extra attention to advanced analytics and data mining? 2. What are the top challenges for multi-channel retailers? Can you think of other industry segments that face similar problems/challenges? 3. What are the sources of data that retailers such as Cabela’s use for their data mining projects? 4. What does it mean to have a “single view of the customer”? How can it be accomplished? 5. What type of analytics help did Cabela’s get from their efforts? Can you think of any other potential benefits of analytics for large-scale retailers like Cabela’s?

Data Mining Concepts and Definitions Why Data Mining? More intense competition at the global scale Recognition of the value in data sources Availability of quality data on customers, vendors, transactions. Web., etc Consolidation and integration of data repositories into data warehouses The exponential increase in data processing and storage capabilities and decrease in cost Movement toward conversion of information resources into nonphysical form Copynight@ 2014 Pearson Education, Inc Slide 4-6

Copyright © 2014 Pearson Education, Inc. Slide 4- 6 Data Mining Concepts and Definitions Why Data Mining? ▪ More intense competition at the global scale. ▪ Recognition of the value in data sources. ▪ Availability of quality data on customers, vendors, transactions, Web, etc. ▪ Consolidation and integration of data repositories into data warehouses. ▪ The exponential increase in data processing and storage capabilities; and decrease in cost. ▪ Movement toward conversion of information resources into nonphysical form

Definition of Data Mining The nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data stored in structured databases Fayyadet al,(1996 Keywords in this definition: Process, nontrivial valid, novel, potentially useful, understandable Data mining: a misnomer? Other names: knowledge extraction, pattern analysis, knowledge discovery, information harvesting, pattern searching, data dredging Copynight@ 2014 Pearson Education, Inc Slide 4-7

Copyright © 2014 Pearson Education, Inc. Slide 4- 7 Definition of Data Mining ▪ The nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data stored in structured databases. - Fayyad et al., (1996) ▪ Keywords in this definition: Process, nontrivial, valid, novel, potentially useful, understandable. ▪ Data mining: a misnomer? ▪ Other names: knowledge extraction, pattern analysis, knowledge discovery, information harvesting, pattern searching, data dredging,…

Data Mining at the Intersection of Many Disciplines Pattern R ecognItion DATA Machine MINING Learning Mathematical Modeling Databases Management Science Information Systems Copynight@ 2014 Pearson Education, Inc Slide 4-8

Copyright © 2014 Pearson Education, Inc. Slide 4- 8 Statistics Management Science & Information Systems Artificial Intelligence Databases Pattern Recognition Machine Learning Mathematical Modeling DATA MINING Data Mining at the Intersection of Many Disciplines

Data Mining Characteristics/Objectives Source of data for dm is often a consolidated data warehouse(not always!) DM environment is usually a client-server or a Web based information systems architecture Data is the most critical ingredient for DM which may include soft/unstructured data The miner is often an end user Striking it rich requires creative thinking Data mining tools capabilities and ease of use are essential(Web, Parallel processing, etc. Copynight@ 2014 Pearson Education, Inc Slide 4-9

Copyright © 2014 Pearson Education, Inc. Slide 4- 9 ▪ Source of data for DM is often a consolidated data warehouse (not always!). ▪ DM environment is usually a client-server or a Web￾based information systems architecture. ▪ Data is the most critical ingredient for DM which may include soft/unstructured data. ▪ The miner is often an end user. ▪ Striking it rich requires creative thinking. ▪ Data mining tools’ capabilities and ease of use are essential (Web, Parallel processing, etc.). Data Mining Characteristics/Objectives

Application Case 4.1 Smarter Insurance: Infinity P&C Improves Customer Service and Combats fraud with Predictive Analytics Questions for Discussion 1. How did Infinity p&c improve customer service with data mining? 2. What were the challenges, the proposed solution, and the obtained results 3. What was their implementation strategy? Why is it important to produce results as early as possible in data mining studies? Copynight@ 2014 Pearson Education, Inc Slide 4-10

Copyright © 2014 Pearson Education, Inc. Slide 4- 10 Application Case 4.1 Smarter Insurance: Infinity P&C Improves Customer Service and Combats Fraud with Predictive Analytics Questions for Discussion 1. How did Infinity P&C improve customer service with data mining? 2. What were the challenges, the proposed solution, and the obtained results? 3. What was their implementation strategy? Why is it important to produce results as early as possible in data mining studies?