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Data Mining: Introduction

Data Mining: Introduction
Data Mining: Introduction © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Why Mine Data Commercial Viewpoint  Lots of data is being collected and warehoused – Web data, ecommerce – purchases at department/ grocery stores – Bank/Credit Card transactions  Computers have become cheaper and more powerful  Competitive Pressure is Strong – Provide better, customized services for an edge (e.g. in Customer Relationship Management) © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Why Mine Data Scientific Viewpoint  Data collected and stored at enormous speeds (GB/hour) – remote sensors on a satellite – telescopes scanning the skies – microarrays generating gene expression data – scientific simulations generating terabytes of data  Traditional techniques infeasible for raw data  Data mining may help scientists – in classifying and segmenting data – in Hypothesis FormationMining Large Data Sets Motivation  There is often information “hidden” in the data that is not readily evident  Human analysts may take weeks to discover useful information  Much of the data is never analyzed at all 4,000,000 3,500,000 3,000,000 The Data Gap 2,500,000 2,000,000 Total new disk (TB) since 1995 1,500,000 1,000,000 Number of 500,000 analysts 0 1995 1996 1997 1998 1999 © F rT o am n: ,S R. teiG nb ro ass chm , K an u, m C. ar Kamath, V. KumIn artr , o “Da duta cti o M nin to in Da g fo ta r S M ci in ein nti gfi c a n d Engineering Applications” 4/18/2004 ‹›What is Data Mining  Many Definitions – Nontrivial extraction of implicit, previously unknown and potentially useful information from data – Exploration analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›What is (not) Data Mining  What is not Data  What is Data Mining Mining – Certain names are more – Look up phone prevalent in certain US number in phone locations (O’Brien, O’Rurke, directory O’Reilly… in Boston area) – Group together similar – Query a Web documents returned by search engine for search engine according to information about their context (e.g. Amazon “Amazon” rainforest, Amazon.com,) © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Origins of Data Mining  Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems  Traditional Techniques may be unsuitable due to Statistics/ Machine Learning/ – Enormity of data AI Pattern Recognition – High dimensionality of data Data Mining – Heterogeneous, distributed nature Database systems of data © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Data Mining Tasks  Prediction Methods – Use some variables to predict unknown or future values of other variables.  Description Methods – Find humaninterpretable patterns that describe the data. From Fayyad, et.al. Advances in Knowledge Discovery and Data Mining, 1996 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Data Mining Tasks...  Classification Predictive  Clustering Descriptive  Association Rule Discovery Descriptive  Sequential Pattern Discovery Descriptive  Regression Predictive  Deviation Detection Predictive © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Classification: Definition  Given a collection of records (training set ) – Each record contains a set of attributes, one of the attributes is the class.  Find a model for class attribute as a function of the values of other attributes.  Goal: previously unseen records should be assigned a class as accurately as possible. – A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it. © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Classification Example Refund Marital Taxable Tid Refund Marital Taxable Cheat Cheat Status Income Status Income 1 Yes Single 125K No No Single 75K 2 No Married 100K No Yes Married 50K 3 No Single 70K No No Married 150K 4 Yes Married 120K No Yes Divorced 90K 5 No Divorced 95K Yes No Single 40K 6 No Married 60K No Test No Married 80K 10 Set 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No Learn Training 10 No Single 90K Yes Model 10 Classifier Set © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Classification: Application 1  Direct Marketing – Goal: Reduce cost of mailing by targeting a set of consumers likely to buy a new cellphone product. – Approach:  Use the data for a similar product introduced before.  We know which customers decided to buy and which decided otherwise. This buy, don’t buy decision forms the class attribute.  Collect various demographic, lifestyle, and company interaction related information about all such customers. – Type of business, where they stay, how much they earn, etc.  Use this information as input attributes to learn a classifier model. From Berry Linoff Data Mining Techniques, 1997 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Classification: Application 2  Fraud Detection – Goal: Predict fraudulent cases in credit card transactions. – Approach:  Use credit card transactions and the information on its accountholder as attributes. – When does a customer buy, what does he buy, how often he pays on time, etc  Label past transactions as fraud or fair transactions. This forms the class attribute.  Learn a model for the class of the transactions.  Use this model to detect fraud by observing credit card transactions on an account. © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Classification: Application 3  Customer Attrition/Churn: – Goal: To predict whether a customer is likely to be lost to a competitor. – Approach: Use detailed record of transactions with each of the past and present customers, to find attributes. – How often the customer calls, where he calls, what timeofthe day he calls most, his financial status, marital status, etc. Label the customers as loyal or disloyal. Find a model for loyalty. From Berry Linoff Data Mining Techniques, 1997 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Classification: Application 4  Sky Survey Cataloging – Goal: To predict class (star or galaxy) of sky objects, especially visually faint ones, based on the telescopic survey images (from Palomar Observatory). – 3000 images with 23,040 x 23,040 pixels per image. – Approach:  Segment the image.  Measure image attributes (features) 40 of them per object.  Model the class based on these features.  Success Story: Could find 16 new high redshift quasars, some of the farthest objects that are difficult to find From Fayyad, et.al. Advances in Knowledge Discovery and Data Mining, 1996 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Classifying Galaxies Courtesy: http://aps.umn.edu Early Class: Attributes: • Image features, • Stages of Formation • Characteristics of light waves received, etc. Intermediate Late Data Size: • 72 million stars, 20 million galaxies • Object Catalog: 9 GB • Image Database: 150 GB © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Clustering Definition  Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that – Data points in one cluster are more similar to one another. – Data points in separate clusters are less similar to one another.  Similarity Measures: – Euclidean Distance if attributes are continuous. – Other Problemspecific Measures. © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Illustrating Clustering Euclidean Distance Based Clustering in 3D space. Intracluster distances Intercluster distances are minimized are maximized © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Clustering: Application 1  Market Segmentation: – Goal: subdivide a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. – Approach:  Collect different attributes of customers based on their geographical and lifestyle related information.  Find clusters of similar customers.  Measure the clustering quality by observing buying patterns of customers in same cluster vs. those from different clusters. © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Clustering: Application 2  Document Clustering: – Goal: To find groups of documents that are similar to each other based on the important terms appearing in them. – Approach: To identify frequently occurring terms in each document. Form a similarity measure based on the frequencies of different terms. Use it to cluster. – Gain: Information Retrieval can utilize the clusters to relate a new document or search term to clustered documents. © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Illustrating Document Clustering  Clustering Points: 3204 Articles of Los Angeles Times.  Similarity Measure: How many words are common in these documents (after some word filtering). Category Total Correctly Articles Placed Financial 555 364 Foreign 341 260 National 273 36 Metro 943 746 Sports 738 573 Entertainment 354 278 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Clustering of SP 500 Stock Data  Observe Stock Movements every day.  Clustering points: StockUP/DOWN  Similarity Measure: Two points are more similar if the events described by them frequently happen together on the same day. We used association rules to quantify a similarity measure. Discovered Clusters Industry Group AppliedMatlDOW N,BayNetworkDown,3COMDOWN, CabletronSysDOWN,CISCODOWN,HPDOWN, 1 DSCCommDOW N,INTELDOWN,LSILogicDOWN, Technology1DOWN MicronTechDOWN,TexasInstDown,TellabsIncDown, NatlSemiconductDOWN,OraclDOWN,SGIDOW N, SunDOW N AppleCompDOW N,AutodeskDOWN,DECDOWN, ADVMicroDeviceDOWN,AndrewCorpDOWN, 2 ComputerAssocDOWN,CircuitCityDOWN, Technology2DOWN CompaqDOWN, EMCCorpDOWN, GenInstDOWN, MotorolaDOW N,MicrosoftDOWN,ScientificAtlDOWN FannieMaeDOWN,FedHomeLoanDOW N, MBNACorpDOWN,MorganStanleyDOWN FinancialDOWN 3 BakerHughesUP,DresserIndsUP,HalliburtonHLDUP, LouisianaLandUP,PhillipsPetroUP,UnocalUP, OilUP 4 SchlumbergerUP © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Association Rule Discovery: Definition  Given a set of records each of which contain some number of items from a given collection; – Produce dependency rules which will predict occurrence of an item based on occurrences of other items. TID Items 1 Bread, Coke, Milk Rules Discovered: 2 Beer, Bread Milk Coke 3 Beer, Coke, Diaper, Milk Diaper, Milk Beer 4 Beer, Bread, Diaper, Milk 5 Coke, Diaper, Milk © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Association Rule Discovery: Application 1  Marketing and Sales Promotion: – Let the rule discovered be Bagels, … Potato Chips – Potato Chips as consequent = Can be used to determine what should be done to boost its sales. – Bagels in the antecedent = Can be used to see which products would be affected if the store discontinues selling bagels. – Bagels in antecedent and Potato chips in consequent = Can be used to see what products should be sold with Bagels to promote sale of Potato chips © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Association Rule Discovery: Application 2  Supermarket shelf management. – Goal: To identify items that are bought together by sufficiently many customers. – Approach: Process the pointofsale data collected with barcode scanners to find dependencies among items. – A classic rule If a customer buys diaper and milk, then he is very likely to buy beer. So, don’t be surprised if you find sixpacks stacked next to diapers © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Association Rule Discovery: Application 3  Inventory Management: – Goal: A consumer appliance repair company wants to anticipate the nature of repairs on its consumer products and keep the service vehicles equipped with right parts to reduce on number of visits to consumer households. – Approach: Process the data on tools and parts required in previous repairs at different consumer locations and discover the cooccurrence patterns. © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Sequential Pattern Discovery: Definition  Given is a set of objects, with each object associated with its own timeline of events, find rules that predict strong sequential dependencies among different events. (A B) (C) (D E)  Rules are formed by first disovering patterns. Event occurrences in the patterns are governed by timing constraints. (A B) (C) (D E) = xg ng = ws = ms © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Sequential Pattern Discovery: Examples  In telecommunications alarm logs, – (InverterProblem ExcessiveLineCurrent) (RectifierAlarm) (FireAlarm)  In pointofsale transaction sequences, – Computer Bookstore: (IntroToVisualC) (C++Primer) (Perlfordummies,TclTk) – Athletic Apparel Store: (Shoes) (Racket, Racketball) (SportsJacket) © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Regression  Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency.  Greatly studied in statistics, neural network fields.  Examples: – Predicting sales amounts of new product based on advetising expenditure. – Predicting wind velocities as a function of temperature, humidity, air pressure, etc. – Time series prediction of stock market indices. © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Deviation/Anomaly Detection  Detect significant deviations from normal behavior  Applications: – Credit Card Fraud Detection – Network Intrusion Detection Typical network traffic at University level may reach over 100 million connections per day © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›Challenges of Data Mining  Scalability  Dimensionality  Complex and Heterogeneous Data  Data Quality  Data Ownership and Distribution  Privacy Preservation  Streaming Data © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹›