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Introduction to Data Mining

Introduction to Data Mining. Motivating Facts. Trends leading to Data Flood More data is generated: Bank, telecom, other business transactions ... Scientific data: astronomy, biology, etc Web, text, and e-commerce Big Data Examples

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Introduction to Data Mining

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  1. Introduction to Data Mining

  2. Motivating Facts Trends leading to Data Flood • More data is generated: • Bank, telecom, other business transactions ... • Scientific data: astronomy, biology, etc • Web, text, and e-commerce Big Data Examples • Europe's Very Long Baseline Interferometry (VLBI) has 16 telescopes, each of which produces 1 Gigabit/second of astronomical data over a 25-day observation session • storage and analysis a big problem • AT&T handles billions of calls per day • so much data, it cannot be all stored -- analysis has to be done “on the fly”, on streaming data

  3. Motivating Facts Largest databases in 2003 • Commercial databases: • Winter Corp. 2003 Survey: France Telecom has largest decision-support DB, ~30TB; AT&T ~ 26 TB • Web • Alexa internet archive: 7 years of data, 500 TB • Google searches 4+ Billion pages, many hundreds TB • IBM WebFountain, 160 TB (2003) • Internet Archive (www.archive.org),~ 300 TB 5 million terabytes created in 2002 • UC Berkeley 2003 estimate: 5 exabytes (5 million terabytes) of new data was created in 2002. www.sims.berkeley.edu/research/projects/how-much-info-2003/ • US produces ~40% of new stored data worldwide

  4. Historical Note: Many Names of Data Mining • Data Fishing, Data Dredging: 1960- • used by Statistician (as bad name) • Data Mining :1990 -- • used DB, business • in 2003 – bad image because of TIA • Knowledge Discovery in Databases (1989-) • used by AI, Machine Learning Community • also Data Archaeology, Information Harvesting, Information Discovery, Knowledge Extraction, ... Currently: Data Mining and Knowledge Discovery are used interchangeably

  5. Big Picture • Lots of hype & misinformation about data mining out there • Data mining is part of a much larger process • 10% of 10% of 10% of 10% • Accuracy not always the most important measure of data mining • The data itself is critical • Algorithms aren’t as important as some people think • If you can’t understand the patterns discovered with data mining, you are unlikely to act on them (or convince others to act)

  6. Defining Data Mining • The automated extraction of hidden predictive information from (large) databases • Three key words: • Automated • Hidden • Predictive • Implicit is a statistical methodology • Data mining lets you be proactive • Prospective rather than Retrospective

  7. Data Mining Is.. • Decision Trees • Nearest Neighbor Classification • Neural Networks • Rule Induction • K-means Clustering Algorithm And more …

  8. Data Mining is Not.. • Brute-force crunching of bulk data • “Blind” application of algorithms • Going to find relationships where none exist • Presenting data in different ways • A database intensive task • A difficult to understand technology requiring an advanced degree in computer science

  9. Data Mining vs. Database • DB’s user knows what is looking for. • DM’s user might/might not know what is looking for. • DB’s answer to query is 100% accurate, if data correct. • DM’s effort is to get the answer as accurate as possible. • DB’s data are retrieved as stored. • DM’s data need to be cleaned (some what) before producing results. • DB’s results are subset of data. • DM’s results are the analysis of the data. • The meaningfulness of the results is not the concern of Database as • it is the main issue in Data Mining.

  10. Convergence of Three Technologies Increasing Computing Power DM Improved Data Collection And Mgmt Statistical & Learning Algorithms

  11. Increased Computing Power • Moore’s law doubles computing power every 18 months • Powerful workstations became common • Cost effective servers provide parallel processing to the mass market • Improved Data Collection • The more data the better (usually)

  12. 3. Improved Algorithms • Techniques have often been waiting for computing technology to catch up • Statisticians already doing “manual data mining” • Good machine learning is just the intelligent application of statistical processes • A lot of data mining research focused on tweaking existing techniques to get small percentage gains

  13. Data Mining: On What Kind of Data? • Relational databases • Data warehouses • Transactional databases • Advanced DB and information repositories • Object-oriented and object-relational databases • Spatial databases • Time-series data and temporal data • Text databases and multimedia databases • Heterogeneous and legacy databases • WWW

  14. Data Mining Application areas • Science • astronomy, bioinformatics, drug discovery, … • Business • advertising, CRM (Customer Relationship management), investments, manufacturing, sports/entertainment, telecom, e-Commerce, targeted marketing, health care, … • Web: • search engines, bots, … • Government • law enforcement, profiling tax cheaters, anti-terror(?)

  15. Data Mining Tasks... • Classification [Predictive] • Clustering [Descriptive] • Association Rule Discovery [Descriptive] • Sequential Pattern Discovery [Descriptive] • Regression [Predictive] • Deviation Detection [Predictive]

  16. Association Rules • Given: • A database of customer transactions • Each transaction is a set of items • Find all rules X => Y that correlate the presence of one set of items X with another set of items Y • Example: 98% of people who purchase diapers and baby food also buy beer. • Any number of items in the consequent/antecedent of a rule • Possible to specify constraints on rules (e.g., find only rules involving expensive imported products)

  17. Confidence and Support • A rule must have some minimum user-specified confidence 1 & 2 => 3 has 90% confidence if when a customer bought 1 and 2, in 90% of cases, the customer also bought 3. • A rule must have some minimum user-specified support 1 & 2 => 3 should hold in some minimum percentage of transactions to have business value

  18. Example • Example: • For minimum support = 50%, minimum confidence = 50%, we have the following rules 1 => 3 with 50% support and 66% confidence 3 => 1 with 50% support and 100% confidence

  19. Problem Decomposition - Example For minimum support = 50% = 2 transactions and minimum confidence = 50% • For the rule 1 => 3: • Support = Support({1, 3}) = 50% • Confidence = Support({1,3})/Support({1}) = 66%

  20. The Apriori Algorithm • Fk : Set of frequent itemsets of size k • Ck : Set of candidate itemsets of size k F1 = {large items} for ( k=1; Fk != 0; k++) do { Ck+1 = New candidates generated from Fk foreach transaction t in the database do Increment the count of all candidates in Ck+1 that are contained in t Fk+1 = Candidates in Ck+1 with minimum support } Answer = Uk Fk

  21. Key Observation • Every subset of a frequent itemset is also frequent=> a candidate itemset in Ck+1 can be pruned if even one of its subsets is not contained in Fk

  22. Apriori - Example Database D F1 C1 Scan D C2 C2 F2 Scan D

  23. Partitioning • Divide database into partitions D1,D2,…,Dp • Apply Apriori to each partition • Any large itemset must be large in at least one partition.

  24. Partitioning Algorithm • Divide D into partitions D1,D2,…,Dp; • For I = 1 to p do • Li = Apriori(Di); • C = L1 …  Lp; • Count C on D to generate L;

  25. Partitioning Example L1 ={{Bread}, {Jelly}, {PeanutButter}, {Bread,Jelly}, {Bread,PeanutButter}, {Jelly, PeanutButter}, {Bread,Jelly,PeanutButter}} D1 L2 ={{Bread}, {Milk}, {PeanutButter}, {Bread,Milk}, {Bread,PeanutButter}, {Milk, PeanutButter}, {Bread,Milk,PeanutButter}, {Beer}, {Beer,Bread}, {Beer,Milk}} D2 S=10%

  26. Partitioning Adv/Disadv • Advantages: • Adapts to available main memory • Easily parallelized • Maximum number of database scans is two. • Disadvantages: • May have many candidates during second scan.

  27. Classification • Given: • Database of tuples, each assigned a class label • Develop a model/profile for each class • Example profile (good credit): • (25 <= age <= 40 and income > 40k) or (married = YES) • Sample applications: • Credit card approval (good, bad) • Bank locations (good, fair, poor) • Treatment effectiveness (good, fair, poor)

  28. Classification Example Sal (<=50K) (>50K) Tid Age Job Salary Class 0 Self 30 30K C Age Class C c 1 Industry 35 40K C (>40) (<=40) 2 Univ. 50 70K C 3 Self 45 60K B Job Class C (Univ., Industry) 4 Univ. 30 70K B (Self) 5 Industry 35 60K A Class B Class A 6 Self 35 60K A 7 Self 30 70K A Training Data Set Sample Decision Tree

  29. Decision Tree • Flow-chart like tree structure • Each node denotes a test on an attribute value • Each branch denotes outcome of the test • Tree leaves represent classes or class distribution • Decision tree can be easily converted into set of classification rules

  30. Example Decision Tree categorical categorical continuous Splitting Attributes class Refund Yes No NO MarSt Married Single, Divorced TaxInc NO < 80K > 80K YES NO The splitting attribute at a node is determined based on the Gini index.

  31. Decision Trees • Pros • Fast execution time • Generated rules are easy to interpret by humans • Scale well for large data sets • Can handle high dimensional data • Cons • Cannot capture correlations among attributes • Consider only axis-parallel cuts

  32. Regression Mapping a data item to a real-value E.g., linear regression Risk score=0.01*(Balance)-0.3*(Age)+4*(HouseOwned)

  33. Clustering • Identifies natural groups or clusters of instances. Example: customer segmentation • Unsupervised learning: Different from classification – clusters are not predefined but are formed based on the data • Objects in each cluster are very similar to each other and are different from those in other clusters.

  34. Customer Attrition: Case Study • Situation: Attrition rate at for mobile phone customers is around 25-30% a year! Task: • Given customer information for the past N months, predict who is likely to attrite next month. • Also, estimate customer value and what is the cost-effective offer to be made to this customer.

  35. Customer Attrition Results • Verizon Wireless built a customer data warehouse • Identified potential attriters • Developed multiple, regional models • Targeted customers with high propensity to accept the offer • Reduced attrition rate from over 2%/month to under 1.5%/month (huge impact, with >30 M subscribers) (Reported in 2003)

  36. Assessing Credit Risk: Case Study • Situation: Person applies for a loan • Task: Should a bank approve the loan? • Note: People who have the best credit don’t need the loans, and people with worst credit are not likely to repay. Bank’s best customers are in the middle

  37. Credit Risk - Results • Banks develop credit models using variety of machine learning methods. • Mortgage and credit card proliferation are the results of being able to successfully predict if a person is likely to default on a loan • Widely deployed in many countries

  38. Successful e-commerce – Case Study • A person buys a book (product) at Amazon.com. • Task: Recommend other books (products) this person is likely to buy • Amazon does clustering based on books bought: • customers who bought “Advances in Knowledge Discovery and Data Mining”, also bought “Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations” • Recommendation program is quite successful

  39. Unsuccessful e-commerce case study (KDD-Cup 2000) • Data: clickstream and purchase data from Gazelle.com, legwear and legcare e-tailer • Q: Characterize visitors who spend more than $12 on an average order at the site • Dataset of 3,465 purchases, 1,831 customers • Very interesting analysis by Cup participants • thousands of hours - $X,000,000 (Millions) of consulting • Total sales -- $Y,000 • Obituary: Gazelle.com out of business, Aug 2000

  40. Genomic Microarrays – Case Study Given microarray data for a number of samples (patients), can we • Accurately diagnose the disease? • Predict outcome for given treatment? • Recommend best treatment?

  41. Example: ALL/AML data • 38 training cases, 34 test, ~ 7,000 genes • 2 Classes: Acute Lymphoblastic Leukemia (ALL) vs Acute Myeloid Leukemia (AML) • Use train data to build diagnostic model ALL AML • Results on test data: • 33/34 correct, 1 error may be mislabeled

  42. Security and Fraud Detection - Case Study • Credit Card Fraud Detection • Detection of Money laundering • FAIS (US Treasury) • Securities Fraud • NASDAQ KDD system • Phone fraud • AT&T, Bell Atlantic, British Telecom/MCI • Bio-terrorism detection at Salt Lake Olympics 2002

  43. Commercial Data Mining Software • It has come a long way in the past seven or eight years • According to IDC, data mining market size of $540M in 2002, $1.5B in 2005 • Depends on what you call “data mining” • Less of a focus towards applications as initially thought • Instead, tool vendors slowly expanding capabilities • Standardization • XML • CWM, PMML, GEML, Clinical Trial Data Model, … • Web services? • Integration • Between applications • Between database & application

  44. What is Currently Happening? • Consolidation • Analytic companies rounding out existing product lines • SPSS buys ISL, NetGenesis • Analytic companies expanding beyond their niche • SAS buys Intrinsic • Enterprise software vendors buying analytic software companies • Oracle buys Thinking Machines • NCR buys Ceres • Niche players are having a difficult time • A lot of consulting • Limited amount of outsourcing • Digimine

  45. Top Data Mining Vendors Today • SAS • 800 Pound Gorilla in the data analysis space • SPSS • Insightful (formerly Mathsoft/S-Plus) • Well respected statistical tools, now moving into mining • Oracle • Integrated data mining into the database • Angoss • One of the first data mining applications (as opposed to tools) • HNC • Very specific analytic solutions • Unica • Great mining technology, focusing less on analytics these days

  46. Standards In Data Mining • Predictive Model Markup Language (PMML) • The Data Mining Group (www.dmg.org) • XML based (DTD) • Java Data Mining API spec request (JSR-000073) • Oracle, Sun, IBM, … • Support for data mining APIs on J2EE platforms • Build, manage, and score models programmatically • OLE DB for Data Mining • Microsoft • Table based • Incorporates PMML • It takes more than an XML standard to get two applications to work together and make users more productive

  47. Privacy Issues • DM applications derive demographics about • customers via • – Credit card use • – Store card • – Subscription • – Book, video, etc rental • – and via more sources… • As the DM results are deemed to be a good • estimate or prediction, one has to be sensitive to • the results not to violate privacy.

  48. Final Comments • Data Mining can be used in any organization that needs to find patterns or relationships in their data. • DM analysts can have a reasonable level of assurance that their Data Mining efforts will render useful, repeatable, and valid results.

  49. Resources and References • Good overview book: • Data Mining Techniquesby Michael Berry and Gordon Linoff • Web: • Knowledge Discovery Nuggets • http://www.kdnuggets.com (ref-tutorials) • http://www.thearling.com (ref-tutorials) • DataMine Mailing List • majordomo@quality.org • send message “subscribe datamine-l”

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