《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，原书第4版）04 Predictive Analytics I：Data Mining Process, Methods, and Algorithms

Business Intelligence, Analytics, and Data Science: A Managerial Perspective Fourth Edition BUSINESS INTELLIGENCE ANALYTICS Chapter 4 AND DATA SCIENCE Predictive Analytics I: Data A Managerial Mining Process, Methods and algorithms Ramesh Sharda Dursun Delen Efraim Turban PEarson Pearson Copyright 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Business Intelligence, Analytics, and Data Science: A Managerial Perspective Fourth Edition Chapter 4 Predictive Analytics I: Data Mining Process, Methods, and Algorithms Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Learning Objectives (1 of2 4.1 Define data mining as an enabling technology for business analytics 4. 2 Understand the objectives and benefits of data mining 4. 3 Become familiar with the wide range of applications of data mining 4.4 Learn the standardized data mining processes 4. 5 Learn different methods and algorithms of data mining Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Learning Objectives (1 of 2) 4.1 Define data mining as an enabling technology for business analytics 4.2 Understand the objectives and benefits of data mining 4.3 Become familiar with the wide range of applications of data mining 4.4 Learn the standardized data mining processes 4.5 Learn different methods and algorithms of data mining

Learning Objectives (2 of 2) 4.6 Build awareness of the existing data mining software tools 4.7 Understand the privacy issues, pitfalls, and myths of data mining Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Learning Objectives (2 of 2) 4.6 Build awareness of the existing data mining software tools 4.7 Understand the privacy issues, pitfalls, and myths of data mining

Opening vignette (I of3 Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime Predictive analytics in law enforcement Policing with less New thinking on cold cases The big picture starts smal Success brings credibility Just for the facts Safer streets for smarter cities Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette (1 of 3) Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime • Predictive analytics in law enforcement – Policing with less – New thinking on cold cases – The big picture starts small – Success brings credibility – Just for the facts – Safer streets for smarter cities

Opening Vignette (2 of3 Discussion Questions 1. Why do law enforcement agencies and departments like Miami-Dade Police Department embrace advanced analytics and data mining? 2. What are the top challenges for law enforcement agencies and departments like Miami-Dade Police Department? Can you think of other challenges(not mentioned in this case) that can benefit from data mining? Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette (2 of 3) Discussion Questions 1. Why do law enforcement agencies and departments like Miami-Dade Police Department embrace advanced analytics and data mining? 2. What are the top challenges for law enforcement agencies and departments like Miami-Dade Police Department? Can you think of other challenges (not mentioned in this case) that can benefit from data mining?

Opening Vignette (3 of3 3. What are the sources of data that law enforcement agencies and departments like Miami-Dade Police Department use for their predictive modeling and data mining projects? 4. What type of analytics do law enforcement agencies and departments like Miami-Dade Police Department use to fight crime? 5. What does the big picture starts smallman in this case? Explain Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette (3 of 3) 3. What are the sources of data that law enforcement agencies and departments like Miami-Dade Police Department use for their predictive modeling and data mining projects? 4. What type of analytics do law enforcement agencies and departments like Miami-Dade Police Department use to fight crime? 5. What does “the big picture starts small” mean in this case? Explain

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 Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved 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 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 Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved 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,…

Figure 4.1 Data Mining is a Blend of Multiple Disciplines atistIcs Science Artificial Information Systems DATA MINING Machine Management earning a Data Pattern Warehousing Recognition Information alvarion Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Figure 4.1 Data Mining is a Blend of Multiple Disciplines

pplication Case 4.1 Visa Is Enhancing the Customer Experience While Reducing Fraud with Predictive Analytics and Data Mining Questions for Discussion 1. What challenges were visa and the rest of the credit card industry facing? 2. How did visa improve customer service while also improving retention of fraud? 3. What is in-memory analytics, and why was it necessary Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Application Case 4.1 Visa Is Enhancing the Customer Experience While Reducing Fraud with Predictive Analytics and Data Mining Questions for Discussion 1. What challenges were Visa and the rest of the credit card industry facing? 2. How did Visa improve customer service while also improving retention of fraud? 3. What is in-memory analytics, and why was it necessary?