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data mining.ppt

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  • 1. Data Mining Chapter 26
  • 2. Chapter 1. Introduction
    • Motivation: Why data mining?
    • What is data mining?
    • Data Mining: On what kind of data?
    • Data mining functionality
    • Are all the patterns interesting?
    • Major issues in data mining
  • 3. Motivation: “Necessity is the Mother of Invention”
    • Data explosion problem
      • Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories
    • We are drowning in data, but starving for knowledge!
    • Solution: Data warehousing and data mining
      • Data warehousing and on-line analytical processing
      • Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases
  • 4. Evolution of Database Technology
    • 1960s:
      • Data collection, database creation, IMS and network DBMS
    • 1970s:
      • Relational data model, relational DBMS implementation
    • 1980s:
      • RDBMS, advanced data models (extended-relational, OO, deductive, etc.) and application-oriented DBMS (spatial, scientific, engineering, etc.)
    • 1990s—2000s:
      • Data mining and data warehousing, multimedia databases, and Web databases
  • 5. What Is Data Mining?
    • Data mining (knowledge discovery in databases):
      • Extraction of interesting ( non-trivial, implicit , previously unknown and potentially useful) information or patterns from data in large databases
    • Alternative names:
      • Data mining: a misnomer?
      • Knowledge discovery(mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.
    • What is not data mining?
      • (Deductive) query processing.
      • Expert systems or small ML/statistical programs
  • 6. Why Data Mining? — Potential Applications
    • Database analysis and decision support
      • Market analysis and management
        • target marketing, customer relation management, market basket analysis, cross selling, market segmentation
      • Risk analysis and management
        • Forecasting, customer retention, improved underwriting, quality control, competitive analysis
      • Fraud detection and management
    • Other Applications
      • Text mining (news group, email, documents)
      • Stream data mining
      • Web mining.
      • DNA data analysis
  • 7. Market Analysis and Management (1)
    • Where are the data sources for analysis?
      • Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies
    • Target marketing
      • Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc.
    • Determine customer purchasing patterns over time
      • Conversion of single to a joint bank account: marriage, etc.
    • Cross-market analysis
      • Associations/co-relations between product sales
      • Prediction based on the association information
  • 8. Market Analysis and Management (2)
    • Customer profiling
      • data mining can tell you what types of customers buy what products (clustering or classification)
    • Identifying customer requirements
      • identifying the best products for different customers
      • use prediction to find what factors will attract new customers
    • Provides summary information
      • various multidimensional summary reports
      • statistical summary information (data central tendency and variation)
  • 9. Corporate Analysis and Risk Management
    • Finance planning and asset evaluation
      • cash flow analysis and prediction
      • contingent claim analysis to evaluate assets
      • cross-sectional and time series analysis (financial-ratio, trend analysis, etc.)
    • Resource planning:
      • summarize and compare the resources and spending
    • Competition:
      • monitor competitors and market directions
      • group customers into classes and a class-based pricing procedure
      • set pricing strategy in a highly competitive market
  • 10. Fraud Detection and Management (1)
    • Applications
      • widely used in health care, retail, credit card services, telecommunications (phone card fraud), etc.
    • Approach
      • use historical data to build models of fraudulent behavior and use data mining to help identify similar instances
    • Examples
      • auto insurance : detect a group of people who stage accidents to collect on insurance
      • money laundering : detect suspicious money transactions (US Treasury's Financial Crimes Enforcement Network)
      • medical insurance : detect professional patients and ring of doctors and ring of references
  • 11. Fraud Detection and Management (2)
    • Detecting inappropriate medical treatment
      • Australian Health Insurance Commission identifies that in many cases blanket screening tests were requested (save Australian $1m/yr).
    • Detecting telephone fraud
      • Telephone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm.
      • British Telecom identified discrete groups of callers with frequent intra-group calls, especially mobile phones, and broke a multimillion dollar fraud.
    • Retail
      • Analysts estimate that 38% of retail shrink is due to dishonest employees.
  • 12. Other Applications
    • Sports
      • IBM Advanced Scout analyzed NBA game statistics (shots blocked, assists, and fouls) to gain competitive advantage for New York Knicks and Miami Heat
    • Astronomy
      • JPL and the Palomar Observatory discovered 22 quasars with the help of data mining
    • Internet Web Surf-Aid
      • IBM Surf-Aid applies data mining algorithms to Web access logs for market-related pages to discover customer preference and behavior pages, analyzing effectiveness of Web marketing, improving Web site organization, etc.
  • 13. Data Mining: A KDD Process
      • Data mining: the core of knowledge discovery process.
    Data Cleaning Data Integration Databases Data Warehouse Knowledge Task-relevant Data Selection Data Mining Pattern Evaluation
  • 14. Steps of a KDD Process
    • Learning the application domain:
      • relevant prior knowledge and goals of application
    • Creating a target data set: data selection
    • Data cleaning and preprocessing: (may take 60% of effort!)
    • Data reduction and transformation:
      • Find useful features, dimensionality/variable reduction, invariant representation.
    • Choosing functions of data mining
      • summarization, classification, regression, association, clustering.
    • Choosing the mining algorithm(s)
    • Data mining : search for patterns of interest
    • Pattern evaluation and knowledge presentation
      • visualization, transformation, removing redundant patterns, etc.
    • Use of discovered knowledge
  • 15. 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 and temporal data
      • Time-series data and stream data
      • Text databases and multimedia databases
      • Heterogeneous and legacy databases
      • WWW
  • 16. Data Mining Functionalities
  • 17. Association Rule Mining
    • Association rule mining:
      • Finding frequent patterns, associations, correlations, or causal structures among sets of items or objects in transaction databases, relational databases, and other information repositories.
      • Frequent pattern : pattern (set of items, sequence, etc.) that occurs frequently in a database
    • Motivation: finding regularities in data
      • What products were often purchased together? — Beer and diapers?!
      • What are the subsequent purchases after buying a PC?
      • What kinds of DNA are sensitive to this new drug?
      • Can we automatically classify web documents?
  • 18. Association Rule Mining (cont.)
    • Itemset X={x 1 , …, x k }
    • Find all the rules X  Y with min confidence and support
      • support , s , probability that a transaction contains X  Y
      • confidence , c, conditional probability that a transaction having X also contains Y .
    • Let min_support = 50%, min_conf = 50%:
      • A  C (50%, 66.7%)
      • C  A (50%, 100%)
    Customer buys diapers Customer buys both Customer buys beer B, E, F 40 A, D 30 A, C 20 A, B, C 10 Items bought Transaction-id
  • 19. Mining Association Rules—an Example
    • For rule A  C :
      • support = support({A}  {C}) = 50%
      • confidence = support({A}  {C})/support({A}) = 66.6%
    Min. support 50% Min. confidence 50% B, E, F 40 A, D 30 A, C 20 A, B, C 10 Items bought Transaction-id 50% {A, C} 50% {C} 50% {B} 75% {A} Support Frequent pattern
  • 20. Apriori: A Candidate Generation-and-test Approach
    • Any subset of a frequent itemset must be frequent
      • if {beer, diaper, nuts} is frequent, so is {beer, diaper}
      • every transaction having {beer, diaper, nuts} also contains {beer, diaper}
    • Apriori pruning principle : If there is any itemset which is infrequent, its superset should not be generated/tested!
    • Method:
      • generate length (k+1) candidate itemsets from length k frequent itemsets, and
      • test the candidates against DB
    • The performance studies show its efficiency and scalability
  • 21. The Apriori Algorithm — An Example Database TDB 1 st scan C 1 L 1 L 2 C 2 C 2 2 nd scan C 3 L 3 3 rd scan B, E 40 A, B, C, E 30 B, C, E 20 A, C, D 10 Items Tid 1 {D} 3 {E} 3 {C} 3 {B} 2 {A} sup Itemset 3 {E} 3 {C} 3 {B} 2 {A} sup Itemset {C, E} {B, E} {B, C} {A, E} {A, C} {A, B} Itemset 1 {A, B} 2 {A, C} 1 {A, E} 2 {B, C} 3 {B, E} 2 {C, E} sup Itemset 2 {A, C} 2 {B, C} 3 {B, E} 2 {C, E} sup Itemset {B, C, E} Itemset 2 {B, C, E} sup Itemset
  • 22. The Apriori Algorithm
    • Pseudo-code :
        • C k : Candidate itemset of size k
        • L k : frequent itemset of size k
        • L 1 = {frequent items};
        • for ( k = 1; L k !=  ; k ++) do begin
        • C k+1 = candidates generated from L k ;
        • for each transaction t in database do
          • increment the count of all candidates in C k+1 that are contained in t
        • L k+1 = candidates in C k+1 with min_support
        • end
        • return  k L k ;
  • 23. Important Details of Apriori
    • How to generate candidates?
      • Step 1: self-joining L k
      • Step 2: pruning
    • Example of Candidate-generation
      • L 3 = { abc, abd, acd, ace, bcd }
      • Self-joining: L 3 *L 3
        • abcd from abc and abd
        • acde from acd and ace
      • Pruning:
        • acde is removed because ade is not in L 3
      • C 4 ={ abcd }
  • 24. How to Generate Candidates?
    • Suppose the items in L k-1 are listed in an order
    • Step 1: self-joining L k-1
      • insert into C k
      • select p.item 1 , p.item 2 , …, p.item k-1 , q.item k-1
      • from L k-1 p, L k-1 q
      • where p.item 1 =q.item 1 , …, p.item k-2 =q.item k-2 , p.item k-1 < q.item k-1
    • Step 2: pruning
      • forall itemsets c in C k do
        • forall (k-1)-subsets s of c do
          • if (s is not in L k-1 ) then delete c from C k
  • 25. Classification and Prediction
      • Finding models (functions) that describe and distinguish classes or concepts for future prediction
      • E.g., classify countries based on climate, or classify cars based on gas mileage
      • Presentation: decision-tree, classification rule, neural network
      • Prediction: Predict some unknown or missing numerical values
  • 26. Classification Process: Model Construction Classification Algorithms IF rank = ‘professor’ OR years > 6 THEN tenured = ‘yes’ Training Data Classifier (Model)
  • 27. Classification Process: Use the Model in Prediction (Jeff, Professor, 4) Tenured? Classifier Testing Data Unseen Data
  • 28. Decision Trees Training set
  • 29. Output: A Decision Tree for “ buys_computer” age? overcast student? credit rating? no yes fair excellent <=30 >40 no no yes yes yes 30..40
  • 30. Cluster and outlier analysis
    • Cluster analysis
      • Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns
      • Clustering based on the principle: maximizing the intra-class similarity and minimizing the interclass similarity
    • Outlier analysis
      • Outlier: a data object that does not comply with the general behavior of the data
      • It can be considered as noise or exception but is quite useful in fraud detection, rare events analysis
  • 31. Clusters and Outliers