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Write about 300-400 words in APA format. Please list in-text citation.Describe how DSS/BI technologies and tools can aid in each phase of decision making? sharda_11e_full_accessible_ppt_04.pptx sharda_11e_full_accessible_ppt_03.pptx Unformatted Attachment Preview Analytics, Data Science and AI: Systems for Decision Support Eleventh Edition Chapter 4 Data Mining Process, Methods, and Algorithms Slide in this Presentation Contain Hyperlinks. JAWS users should be able to get a list of links by using INSERT+F7 Copyright © 2020, 2015, 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 Copyright © 2020, 2015, 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 Copyright © 2020, 2015, 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 Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette (2 of 3) Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime 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? Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette (3 of 3) Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime Discussion Questions (continued) 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. Copyright © 2020, 2015, 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. Copyright © 2020, 2015, 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,… Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Data Mining Is a Blend of Multiple Disciplines Figure 4.1 Data Mining Is a Blend of Multiple Disciplines. Copyright © 2020, 2015, 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? Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved 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.) Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved How Data Mining Works • DM extract patterns from data – Pattern? A mathematical (numeric and/or symbolic) relationship among data items • Types of patterns – Association – Prediction – Cluster (segmentation) – Sequential (or time series) relationships Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Application Case 4.2 American Honda Uses Advanced Analytics to Improve Warranty Claims Questions for Discussion: 1. How does American Honda use analytics to improve warranty claims? 2. In addition to warranty claims, for what other purposes does American Honda use advanced analytics methods? 3. Can you think of other uses of advanced analytics in the automotive industry? You can search the Web to find some answers to this question. Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved A Taxonomy for Data Mining Figure 4.2 A Simple Taxonomy for Data Mining Tasks, Methods, and Algorithms. Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Other Data Mining Patterns/Tasks • Time-series forecasting – Part of the sequence or link analysis? • Visualization – Another data mining task? – Covered in Chapter 3 • Data Mining versus Statistics – Are they the same? – What is the relationship between the two? Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Data Mining Applications (1 of 4) • Customer Relationship Management – Maximize return on marketing campaigns – Improve customer retention (churn analysis) – Maximize customer value (cross-, up-selling) – Identify and treat most valued customers • Banking & Other Financial – Automate the loan application process – Detecting fraudulent transactions – Maximize customer value (cross-, up-selling) – Optimizing cash reserves with forecasting Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Data Mining Applications (2 of 4) • Retailing and Logistics – Optimize inventory levels at different locations – Improve the store layout and sales promotions – Optimize logistics by predicting seasonal effects – Minimize losses due to limited shelf life • Manufacturing and Maintenance – Predict/prevent machinery failures – Identify anomalies in production systems to optimize the use manufacturing capacity – Discover novel patterns to improve product quality Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Data Mining Applications (3 of 4) • Brokerage and Securities Trading – Predict changes on certain bond prices – Forecast the direction of stock fluctuations – Assess the effect of events on market movements – Identify and prevent fraudulent activities in trading • Insurance – Forecast claim costs for better business planning – Determine optimal rate plans – Optimize marketing to specific customers – Identify and prevent fraudulent claim activities Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Data Mining Applications (4 of 4) • Computer hardware and software • Science and engineering • Government and defense • Homeland security and law enforcement • Travel, entertainment, sports • Healthcare and medicine • Sports,… virtually everywhere… Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Application Case 4.3 Predictive Analytic and Data Mining Help Stop Terrorist Funding Questions for Discussion: 1. How can data mining be used to fight terrorism? Comment on what else can be done beyond what is covered in this short application case. 2. Do you think data mining, although essential for fighting terrorist cells, also jeopardizes individuals’ rights of privacy? Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Data Mining Process • A manifestation of the best practices • A systematic way to conduct DM projects • Moving from Art to Science for DM project • Everybody has a different version • Most common standard processes: – CRISP-DM (Cross-Industry Standard Process for Data Mining) – SEMMA (Sample, Explore, Modify, Model, and Assess) – KDD (Knowledge Discovery in Databases) Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Data Mining Process: CRISP-DM (1 of 2) • Cross Industry Standard Process for Data Mining • Proposed in 1990s by a European consortium • Composed of six consecutive steps – – – – – – Step 1: Business Understanding Step 2: Data Understanding Step 3: Data Preparation Step 4: Model Building Step 5: Testing and Evaluation Step 6: Deployment  Accounts for   ~85\% of total  project time  Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Data Mining Process: CRISP-DM (2 of 2) • Figure 4.3 The SixStep CRISP-DM Data Mining Process. → • The process is highly repetitive and experimental (DM: art versus science?) Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Data Mining Process: SEMMA Figure 4.5 SEMMA Data Mining Process. • Developed by SAS Institute Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Data Mining Process: KDD Figure 4.6 KDD (Knowledge Discovery in Databases) Process. Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Which Data Mining Process is the Best? Figure 4.7 Ranking of Data Mining Methodologies/Processes. Source: Used with permission from KDnuggets.com. Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Application Case 4.4 Data Mining Helps in Cancer Research Questions for Discussion 1. How can data mining be used for ultimately curing illnesses like cancer? 2. What do you think are the promises and major challenges for data miners in contributing to medical and biological research endeavors? Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Data Mining Methods: Classification • Most frequently used DM method • Part of the machine-learning family • Employ supervised learning • Learn from past data, classify new data • The output variable is categorical (nominal or ordinal) in nature • Classification versus regression? • Classification versus clustering? Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Assessment Methods for Classification • Predictive accuracy – Hit rate • Speed – Model building versus predicting/usage speed • Robustness • Scalability • Interpretability – Transparency, explainability Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Accuracy of Classification Models • In classification problems, the primary source for accuracy estimation is the confusion matrix Accuracy = TP + TN TP + TN + FP + FN True Positive Rate = TP TP + FN True Negative Rate = TN TN + FP Precision = TP TP + FP Recall = TP TP + FN Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Estimation Methodologies for Classification: Single/Simple Split • Simple split (or holdout or test sample estimation) – Split the data into 2 mutually exclusive sets: training (~70\%) and testing (30\%) – For Neural Networks, the data is split into three subsets (training [~60\%], validation [~20\%], testing [~20\%]) Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Estimation Methodologies for Classification: k-Fold Cross Validation • Data is split into k mutual subsets and k number training/testing experiments are conducted Figure 4.10 A Graphical Depiction of k-Fold Cross-Validation. Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Additional Estimation Methodologies for Classification • Leave-one-out – Similar to k-fold where k = number of samples • Bootstrapping – Random sampling with replacement • Jackknifing – Similar to leave-one-out • Area Under the ROC Curve (AUC) – ROC: receiver operating characteristics (a term borrowed from radar image processing) Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Area Under the ROC Curve (AUC) (1 of 2) • Works with binary classification Figure 4.11 A Sample ROC Curve. Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Area Under the ROC Curve (AUC) (2 of 2) • Produces values from 0 to 1.0 • Random chance is 0.5 and perfect classification is 1.0 • Produces good a assessment for skewed class distributions too! Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Classification Techniques • Decision tree analysis • Statistical analysis • Neural networks • Support vector machines • Case-based reasoning • Bayesian classifiers • Genetic algorithms • Rough sets Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Decision Trees (1 of 2) • Employs a divide-and-conquer method • Recursively divides a training set until each division consists of examples from one class: A general 1. Create a root node and assign all of the training data to it. algorithm (steps) for 2. Select the best splitting attribute. building a 3. Add a branch to the root node for each value of decision the split. Split the data into mutually exclusive tree subsets along the lines of the specific split. 4. Repeat the steps 2 and 3 for each and every leaf node until the stopping criteria is reached. Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Decision Trees (2 of 2) • DT algorithms mainly differ on 1. Splitting criteria ▪ Which variable, what value, etc. 2. Stopping criteria ▪ When to stop building the tree 3. Pruning (generalization method) ▪ Pre-pruning versus post-pruning • Most popular DT algorithms include – ID3, C4.5, C5; CART; CHAID; M5 Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Ensemble Models for Predictive Analytics • Produces more robust and reliable prediction models Figure 4.12 Graphical Illustration of a Heterogeneous Ensemble. Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Application Case 4.5 Influence Health Uses Advanced Predictive Analytics to Focus on the Factors That Really Influence People’s Healthcare Decisions Questions for Discussion: 1. What did Influence Health do? 2. What were the challenges, the proposed solutions, and the obtained results? 3. How can data mining help companies in the healthcare industry (in ways other than the ones mentioned in this case)? Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Cluster Analysis for Data Mining (1 of 4) • Used for automatic identification of natural groupings of things • Part of the machine-learning family • Employ unsupervised learning • Learns the clusters of things from past data, then assigns new instances • There is not an output/target variable • In marketing, it is also known as segmentation Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Cluster Analysis for Data Mining (2 of 4) • Clustering results may be used to – Identify natural groupings of customers – Identify rules for assigning new cases to classes for targeting/diagnostic purposes – Provide characterization, definition, labeling of populations – Decrease the size and complexity of problems for other data mining methods – Identify outliers in a specific domain (e.g., rare-event detection) Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Cluster Analysis for Data Mining (3 of 4) • Analysis methods – Statistical methods (including both hierarchical and nonhierarchical), such as k-means, k-modes, and so on. – Neural networks (adaptive resonance theory [ART], self-organizing map [SOM]) – Fuzzy logic (e.g., fuzzy c-means algorithm) – Genetic algorithms • How many clusters? Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Cluster Analysis for Data Mining (4 of 4) • k-Means Clustering Algorithm – k: pre-determined number of clusters – Algorithm (Step 0: determine value of k) Step 1: Randomly generate k random points as initial cluster centers. Step 2: Assign each point to the nearest cluster center. Step 3: Re-compute the new cluster centers. Repetition step: Repeat steps 3 and 4 until some convergence criterion is met (usually that the assignment of points to clusters becomes stable). Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Cluster Analysis for Data Mining k-Means Clustering Algorithm Figure 4.13 A Graphical Illustration of the Steps in the k-Means Algorithm. Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Association Rule Mining (1 of 6) • A very popular DM method in business • Finds interesting relationships (affinities) between variables (items or events) • Part of machine learning family • Employs unsupervised learning • There is no output variable • Also known as market basket analysis • Often used as an example to describe DM to ordinary people, such as the famous “relationship between diapers and beers!” Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Association Rule Mining (2 of 6) • Input: the simple point-of-sale transaction data • Output: Most frequent affinities among items • Example: according to the transaction data… “Customer who bought a lap-top computer and a virus protection software, also bought extended service plan 70 percent of the time. • How do you use such a pattern/knowledge? – Put the items next to each other – Promote the items as a package – Place items far apart from each other! Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Association Rule Mining (3 of 6) • A representative applications of association rule mining include – In business: cross-marketing, cross-selling, store design, catalog design, e-commerce site design, optimization of online advertising, product pricing, and sales/promotion configuration – In medicine: relationships between symptoms and illnesses; diagnosis and patient characteristics and treatments (to be used in medical DSS); and genes and their functions (to be used in genomics projects) – … Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Association Rule Mining (4 of 6) • Are all association rules interesting and useful? A Generic Rule: X  Y [S\%, C\%] X, Y: products and/or services X: Left-hand-side (LHS) Y: Right-hand-side (RHS) S: Support: how often X and Y go together C: Confidence: how often Y go together with the X Example: {Laptop Computer, Antivirus Software}  {Extended Service Plan} [30\%, 70\%] Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Association Rule Mining (5 of 6) • Several algorithms are developed for discovering (identifying) association rules – Apriori – Eclat – FP-Growth – + Derivatives and hybrids of the three • The ... Purchase answer to see full attachment