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?