Decision Support and Business Intelligence Systems (9th Ed., Prentice Hall) Chapter 5: Data Mining for Business Intelligence 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    5-2 CRISP-DM, SEMMA, KDD, … Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 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   5-3 Commercial versus free/open source Understand the pitfalls and myths of data mining Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Opening Vignette: “Data Mining Goes to Hollywood!”  Decision situation  Problem  Proposed solution  Results  Answer and discuss the case questions 5-4 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Opening Vignette: Data Mining Goes to Hollywood! Class No. Range (in $Millions) 1 2 3 1 > 10 (Flop) < 10 < 20 Dependent Variable Independent Variables A Typical Classification Problem 5-5 4 5 6 7 8 9 > 20 > 40 > 65 > 100 > 150 > 200 < 40 < 65 < 100 < 150 < 200 (Blockbuster) Independent Variable Number of Possible Values Values MPAA Rating 5 G, PG, PG-13, R, NR Competition 3 High, Medium, Low Star value 3 High, Medium, Low Genre 10 Sci-Fi, Historic Epic Drama, Modern Drama, Politically Related, Thriller, Horror, Comedy, Cartoon, Action, Documentary Special effects 3 High, Medium, Low Sequel 1 Yes, No Number of screens 1 Positive integer Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Opining Vignette: Data Mining Goes to Hollywood! The DM Process Map in PASW 5-6 Model Development process Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Model Assessment process Opening Vignette: Data Mining Goes to Hollywood! Prediction Models Individual Models Performance Measure SVM ANN Ensemble Models C&RT Random Forest Boosted Tree Fusion (Average) Count (Bingo) 192 182 140 189 187 194 Count (1-Away) 104 120 126 121 104 120 Accuracy (% Bingo) 55.49% 52.60% 40.46% 54.62% 54.05% 56.07% Accuracy (% 1-Away) 85.55% 87.28% 76.88% 89.60% 84.10% 90.75% 0.93 0.87 1.05 0.76 0.84 0.63 Standard deviation * Training set: 1998 – 2005 movies; Test set: 2006 movies 5-7 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Why Data Mining?       5-8 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 © 2011 Pearson Education, Inc. Publishing as Prentice Hall Definition of Data Mining     5-9 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 © 2011 Pearson Education, Inc. Publishing as Prentice Hall Data Mining at the Intersection of Many Disciplines ial e Int tis tic s c tifi Ar Pattern Recognition en Sta llig Mathematical Modeling Machine Learning Databases Management Science & Information Systems 5-10 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall ce DATA MINING Data Mining Characteristics/Objectives       5-11 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 © 2011 Pearson Education, Inc. Publishing as Prentice Hall Data in Data Mining    Data: a collection of facts usually obtained as the result of experiences, observations, or experiments Data may consist of numbers, words, images, … Data: lowest level of abstraction (from which information and knowledge are derived) Data - DM with different data types? Categorical Nominal 5-12 - Other data types? Numerical Ordinal Interval Ratio Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall What Does DM Do?  DM extract patterns from data   Types of patterns     5-13 Pattern? A mathematical (numeric and/or symbolic) relationship among data items Association Prediction Cluster (segmentation) Sequential (or time series) relationships Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A Taxonomy for Data Mining Tasks Data Mining Learning Method Popular Algorithms Supervised Classification and Regression Trees, ANN, SVM, Genetic Algorithms Classification Supervised Decision trees, ANN/MLP, SVM, Rough sets, Genetic Algorithms Regression Supervised Linear/Nonlinear Regression, Regression trees, ANN/MLP, SVM Unsupervised Apriory, OneR, ZeroR, Eclat Link analysis Unsupervised Expectation Maximization, Apriory Algorithm, Graph-based Matching Sequence analysis Unsupervised Apriory Algorithm, FP-Growth technique Unsupervised K-means, ANN/SOM Prediction Association Clustering Outlier analysis 5-14 Unsupervised K-means, Expectation Maximization (EM) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Data Mining Tasks (cont.)  Time-series forecasting   Visualization   Another data mining task? Types of DM   5-15 Part of sequence or link analysis? Hypothesis-driven data mining Discovery-driven data mining Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Data Mining Applications  Customer Relationship Management      Banking and Other Financial     5-16 Maximize return on marketing campaigns Improve customer retention (churn analysis) Maximize customer value (cross-, up-selling) Identify and treat most valued customers Automate the loan application process Detecting fraudulent transactions Maximize customer value (cross-, up-selling) Optimizing cash reserves with forecasting Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Data Mining Applications (cont.)  Retailing and Logistics      Manufacturing and Maintenance    5-17 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 Predict/prevent machinery failures Identify anomalies in production systems to optimize the use manufacturing capacity Discover novel patterns to improve product quality Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Data Mining Applications  Brokerage and Securities Trading      Insurance     5-18 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 Forecast claim costs for better business planning Determine optimal rate plans Optimize marketing to specific customers Identify and prevent fraudulent claim activities Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Data Mining Applications (cont.)           5-19 Computer hardware and software Science and engineering Government and defense Homeland security and law enforcement Travel industry Healthcare Highly popular application areas for data mining Medicine Entertainment industry Sports Etc. Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Data Mining Process     A manifestation of best practices A systematic way to conduct DM projects Different groups has different versions Most common standard processes:    5-20 CRISP-DM (Cross-Industry Standard Process for Data Mining) SEMMA (Sample, Explore, Modify, Model, and Assess) KDD (Knowledge Discovery in Databases) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Data Mining Process Source: KDNuggets.com, August 2007 5-21 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Data Mining Process: CRISP-DM 1 Business Understanding 2 Data Understanding 3 Data Preparation Data Sources 6 4 Deployment Model Building 5 Testing and Evaluation 5-22 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Data Mining Process: CRISP-DM Step Step Step Step Step Step  5-23 1: 2: 3: 4: 5: 6: Business Understanding Data Understanding Data Preparation (!) Model Building Testing and Evaluation Deployment Accounts for ~85% of total project time The process is highly repetitive and experimental (DM: art versus science?) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Data Preparation – A Critical DM Task Real-world Data Data Consolidation · · · Collect data Select data Integrate data Data Cleaning · · · Impute missing values Reduce noise in data Eliminate inconsistencies Data Transformation · · · Normalize data Discretize/aggregate data Construct new attributes Data Reduction · · · Reduce number of variables Reduce number of cases Balance skewed data Well-formed Data 5-24 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Data Mining Process: SEMMA Sample (Generate a representative sample of the data) Assess Explore (Evaluate the accuracy and usefulness of the models) (Visualization and basic description of the data) SEMMA 5-25 Model Modify (Use variety of statistical and machine learning models ) (Select variables, transform variable representations) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Data Mining Methods: Classification        5-26 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 © 2011 Pearson Education, Inc. Publishing as Prentice Hall Assessment Methods for Classification  Predictive accuracy   Speed     Model building; predicting Robustness Scalability Interpretability  5-27 Hit rate Transparency, explainability Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Accuracy of Classification Models  In classification problems, the primary source for accuracy estimation is the confusion matrix Predicted Class Negative Positive True Class Positive Negative 5-28 True Positive Count (TP) False Positive Count (FP) Accuracy  TP  TN TP  TN  FP  FN True Positive Rate  TP TP  FN True Negative Rate  False Negative Count (FN) True Negative Count (TN) Precision  TP TP  FP Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall TN TN  FP Recall  TP TP  FN Estimation Methodologies for Classification  Simple split (or holdout or test sample estimation)  Split the data into 2 mutually exclusive sets training (~70%) and testing (30%) 2/3 Training Data Model Development Classifier Preprocessed Data 1/3 Testing Data  Model Assessment (scoring) Prediction Accuracy For ANN, the data is split into three sub-sets (training [~60%], validation [~20%], testing [~20%]) 5-29 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Estimation Methodologies for Classification  k-Fold Cross Validation (rotation estimation)      Other estimation methodologies   5-30 Split the data into k mutually exclusive subsets Use each subset as testing while using the rest of the subsets as training Repeat the experimentation for k times Aggregate the test results for true estimation of prediction accuracy training Leave-one-out, bootstrapping, jackknifing Area under the ROC curve Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Estimation Methodologies for Classification – ROC Curve 1 0.9 True Positive Rate (Sensitivity) 0.8 A 0.7 B 0.6 C 0.5 0.4 0.3 0.2 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 False Positive Rate (1 - Specificity) 5-31 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 0.9 1 Classification Techniques         5-32 Decision tree analysis Statistical analysis Neural networks Support vector machines Case-based reasoning Bayesian classifiers Genetic algorithms Rough sets Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision Trees   A general algorithm for decision tree building Employs the divide and conquer method Recursively divides a training set until each division consists of examples from one class 1. 2. 3. 4. 5-33 Create a root node and assign all of the training data to it Select the best splitting attribute Add a branch to the root node for each value of the split. Split the data into mutually exclusive subsets along the lines of the specific split Repeat the steps 2 and 3 for each and every leaf node until the stopping criteria is reached Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision Trees  DT algorithms mainly differ on  Splitting criteria     Stopping criteria   Pre-pruning versus post-pruning Most popular DT algorithms include  5-34 When to stop building the tree Pruning (generalization method)   Which variable to split first? What values to use to split? How many splits to form for each node? ID3, C4.5, C5; CART; CHAID; M5 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision Trees  Alternative splitting criteria  Gini index determines the purity of a specific class as a result of a decision to branch along a particular attribute/value   Information gain uses entropy to measure the extent of uncertainty or randomness of a particular attribute/value split   5-35 Used in CART Used in ID3, C4.5, C5 Chi-square statistics (used in CHAID) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Cluster Analysis for Data Mining       5-36 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 variable Also known as segmentation Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Cluster Analysis for Data Mining  Clustering results may be used to      5-37 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 © 2011 Pearson Education, Inc. Publishing as Prentice Hall Cluster Analysis for Data Mining  Analysis methods      5-38 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 Divisive versus Agglomerative methods Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Cluster Analysis for Data Mining  How many clusters?   There is not a “truly optimal” way to calculate it Heuristics are often used      Most cluster analysis methods involve the use of a distance measure to calculate the closeness between pairs of items  5-39 Look at the sparseness of clusters Number of clusters = (n/2)1/2 (n: no of data points) Use Akaike information criterion (AIC) Use Bayesian information criterion (BIC) Euclidian versus Manhattan (rectilinear) distance Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Cluster Analysis for Data Mining  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) 5-40 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Cluster Analysis for Data Mining k-Means Clustering Algorithm Step 1 5-41 Step 2 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Step 3 Association Rule Mining        5-42 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 © 2011 Pearson Education, Inc. Publishing as Prentice Hall Association Rule Mining     Input: the simple point-of-sale transaction data Output: Most frequent affinities among items Example: according to the transaction data… “Customer who bought a laptop computer and a virus protection software, also bought extended service plan 70 percent of the time." How do you use such a pattern/knowledge?    5-43 Put the items next to each other for ease of finding Promote the items as a package (do not put one on sale if the other(s) are on sale) Place items far apart from each other so that the customer has to walk the aisles to search for it, and by doing so potentially seeing and buying other items Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Association Rule Mining  A representative applications of association rule mining include   5-44 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 © 2011 Pearson Education, Inc. Publishing as Prentice Hall Association Rule Mining  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%] 5-45 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Association Rule Mining  Algorithms are available for generating association rules      5-46 Apriori Eclat FP-Growth + Derivatives and hybrids of the three The algorithms help identify the frequent item sets, which are, then converted to association rules Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Association Rule Mining  Apriori Algorithm   Finds subsets that are common to at least a minimum number of the itemsets uses a bottom-up approach    5-47 frequent subsets are extended one item at a time (the size of frequent subsets increases from one-item subsets to two-item subsets, then three-item subsets, and so on), and groups of candidates at each level are tested against the data for minimum support see the figure… Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Association Rule Mining  Apriori Algorithm Raw Transaction Data 5-48 One-item Itemsets Two-item Itemsets Three-item Itemsets Transaction No SKUs (Item No) Itemset (SKUs) Support Itemset (SKUs) Support Itemset (SKUs) Support 1 1, 2, 3, 4 1 3 1, 2 3 1, 2, 4 3 1 2, 3, 4 2 6 1, 3 2 2, 3, 4 3 1 2, 3 3 4 1, 4 3 1 1, 2, 4 4 5 2, 3 4 1 1, 2, 3, 4 2, 4 5 1 2, 4 3, 4 3 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Data Mining Software SPSS PASW Modeler (formerly Clementine) RapidMiner SAS / SAS Enterprise Miner Microsoft Excel R Your own code  Commercial       Weka (now Pentaho) SPSS - PASW (formerly Clementine) SAS - Enterprise Miner IBM - Intelligent Miner StatSoft – Statistical Data Miner … many more Free and/or Open Source   KXEN Weka RapidMiner… MATLAB Other commercial tools KNIME Microsoft SQL Server Other free tools Zementis Oracle DM Statsoft Statistica Salford CART, Mars, other Orange Angoss C4.5, C5.0, See5 Bayesia Insightful Miner/S-Plus (now TIBCO) Megaputer Viscovery Clario Analytics Alone Thinkanalytics Source: KDNuggets.com, May 2009 5-49 Total (w/ others) Miner3D 0 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 20 40 60 80 100 120 Data Mining Myths  Data mining …       5-50 provides instant solutions/predictions is not yet viable for business applications requires a separate, dedicated database can only be done by those with advanced degrees is only for large firms that have lots of customer data is another name for the good-old statistics Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Common Data Mining Mistakes 1. 2. 3. 4. 5. 5-51 Selecting the wrong problem for data mining Ignoring what your sponsor thinks data mining is and what it really can/cannot do Not leaving insufficient time for data acquisition, selection and preparation Looking only at aggregated results and not at individual records/predictions Being sloppy about keeping track of the data mining procedure and results Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Common Data Mining Mistakes 6. 7. 8. 9. 10. 5-52 Ignoring suspicious (good or bad) findings and quickly moving on Running mining algorithms repeatedly and blindly, without thinking about the next stage Naively believing everything you are told about the data Naively believing everything you are told about your own data mining analysis Measuring your results differently from the way your sponsor measures them Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall End of the Chapter  5-53 Questions / Comments… Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 5-54 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
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