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Data Mining

Data Mining. Week Material Week Introduction Week 2 Data Warehouse & OLAP Week 3 Data Preprocessing Week 4 Data Mining Languages Week 5 Concept Description Week 6 Statistic Week 7-8 Association Rules

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Data Mining

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  1. Data Mining

  2. Week Material Week Introduction Week 2 Data Warehouse & OLAP Week 3 Data Preprocessing Week 4 Data Mining Languages Week 5 Concept Description Week 6 Statistic Week 7-8 Association Rules Week 9-10 Classification Week 11-12 Cluster Analysis Week 13-14 Mining Complex Data Week 15 Applications Midterm 3/2/04 Project due 4/29/04 Final 5/6/04 No Late Submissions are allowed Syllabus

  3. Textbook and Other Reading Materials • Textbook: Data Mining: Concepts and Techniques by Jiawei Han and Micheline Kamber, Morgan Kaufman, 2001 • Other texts that I may use from time to time: • Data Mining –Introductory and Advanced Topics by Margaret H. Duhnam, Pearson Education,Inc, 2003 • Principles of Data Mining by David Hand, Heikki Mannila, and Padhriac Smyth, MIT Press 2001 • Papers: VLDB, SIGMOD, and SIGKDD Proceedings`

  4. Introduction • Motivation. • What is data mining? • Data mining functionality • Are all the patterns interesting? • Classification of data mining systems

  5. Motivation: • Huge amount of databases and web pages make information extraction next to impossible (remember the favored statement: I will bury them in data!) • Inability of many other disciplines: (statistic, AI, information retrieval) to have scalable algorithms to extract information and/or rules from the databases • Necessity to find relationships among data

  6. Appetizer • Consider a file consisting of 24471 records. File contains at least two condition attributes: A and D

  7. Appetizer (con’t) • Probability that person has A: P(A)=0.6, P(D)=0.02 • Conditional probability that person has D provided it has A: P(D|A) = P(AD)/P(A)=(272/24471)/.6 = .02 • P(A|D) = P(AD)/P(D)= .56 • What can we say about dependencies between A and D?

  8. Appetizer(3) • So far we did not ask anything that statistics would not have ask. So Data Mining another word for statistic? • We hope that the response will be resounding NO • The major difference is that statistical methods work with random data samples, whereas the data in databases is not necessarily random • The second difference is the size of the data set • The third data is that statistical samples do not contain “dirty” data

  9. STATISTIC is NOT DATA MINING • Originally data mining was a statistician term for overusing data to create possible wrong inferences. • Famous example of wrong inferences is in parapsychology on ECP (extrasensory perception) • If there are too many conclusions from the data, then some will be certainly true. • Data Mining is a discovery of UNEXPECTED data correlations

  10. What Is Data Mining? • Data mining (knowledge discovery in databases): • Extraction of interesting information or patterns from data in large databases • Alternative names and their “inside stories”: • 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 • Statistics • Artificial Intelligence

  11. Data Mining: Process Knowledge • Data mining: the core of knowledge discovery process. Pattern Evaluation Data Mining Task-relevant Data Selection Data Warehouse Data Cleaning Data Integration Databases

  12. What Is Data Mining – Steps in the DM Process • Data cleaning, noise removal • Data Integration- data warehousing techniques, OLAP • Data Relevancy decision • Data Transformation (data qube, aggregation and summarization) • Pattern evaluations • Results presentation

  13. What is DM: 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) and Web analysis. • Intelligent query answering

  14. 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

  15. 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)

  16. 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

  17. 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

  18. Fraud Detection and Management (2) • Detecting inappropriate medical treatment • 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.

  19. 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.

  20. Architecture of a Typical Data Mining System Graphical user interface Pattern evaluation Data mining engine Knowledge-base Database or data warehouse server Filtering Data cleaning & data integration Data Warehouse Databases

  21. Data Mining System Architecture • Database, data warehouse, data files- set of data to be mined. Data Cleaning and data integration may be performed at this stage • Database or data warehouse server is responsible for fetching relevant data. How to define relevancy? • Knowledge Base – Domain knowledge that drives a search for patterns. Concept hierarchy, User Beliefs, Interestingness Constraints • Data Mining Engine-Functional algorithms to perform a search for domain experts • Pattern Evaluation – Use knowledge base and other methods to narrow search for domain patters • GUI – Communicator between users and data mining system

  22. Data Mining: On What Kind of Data? • Relational databases – Universal relation vs Multirelational search • 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

  23. Data Mining: On What Kind of Data? • Attribute Types: • Categorical – attribute that has a finite number of values • Ordinal – attributes can be ordered by their values • Attribute Transformations: • Continuing - attribute that may have infinite but countable set of values. These attributes always can be ordered • Interval scale • Boolean • Nominal – attributes that cannot be ordered by their values • Operational - example measurement of programming productivity as am(n+m)log(a+b)/2b, where a is the number of unique operators,b is the number of unique operands, n-number of total operators occurences and m the number of total operands occurences

  24. Data Mining Tasks • Association (correlation and causality) • Multi-dimensional vs. single-dimensional association • age(X, “20..29”) ^ income(X, “20..29K”) -> buys(X, “PC”) [support = 2%, confidence = 60%] • contains(T, “computer”) -> contains(x, “software”) [1%, 75%] • What is support? – the percentage of the tuples in the database that have age between 20 and 29 and income between 20K and 29K and buying PC • What is confidence? – the probability that if person is between 20 and 29 and income between 20K and 29K then it buys PC • Clustering (getting data that are close together into the same cluster. • What does “close together” means?

  25. Distances between data • Distance between data is a measure of dissimilarity between data. d(i,j)>=0; d(i,j) = d(j,i); d(i,j)<= d(i,k) + d(k,j) • Euclidean distance: <x1,x2, … xk> and <y1,y2,…yk> • Standardize variables by finding standard deviation and dividing each xi by standard deviation of X • Covariance(X,Y)=1/k(Sum(xi-mean(x))(y(I)-mean(y)) • Boolean variables and their distances

  26. Data Mining Tasks • 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 • Trend and evolution analysis • Trend and deviation: regression analysis • Sequential pattern mining, periodicity analysis • Similarity-based analysis • Other pattern-directed or statistical analyses

  27. Are All the “Discovered” Patterns Interesting? • A data mining system/query may generate thousands of patterns, not all of them are interesting. • Suggested approach: Human-centered, query-based, focused mining • Interestingness measures: A pattern is interesting if it is easily understood by humans, valid on new or test data with some degree of certainty, potentially useful, novel, or validates some hypothesis that a user seeks to confirm • Objective vs. subjective interestingness measures: • Objective: based on statistics and structures of patterns, e.g., support, confidence, etc. • Subjective: based on user’s belief in the data, e.g., unexpectedness, novelty, actionability, etc.

  28. Are All the “Discovered” Patterns Interesting? - Example coffee 0 1 tea 5 5 20 25 0 70 75 Conditional probability that if one buys coffee, one also buys tea is 2/9 Conditional probability that if one buys tea she also buys coffee is 20/25=.8 However, the probability that she buys coffee is .9 So, is it significant inference that if customer buys tea she also buys coffee? Is buying tea and coffee independent activities?

  29. How to measure Interestingness • RI = | X , Y| - |X||Y|/N • Support and Confidence: |X Y|/N – support and |X Y|/|X| -confidence of X->Y • Chi^2: (|XY| - E(|XY|)) ^2 /E(|XY|); • J(X->Y) = P(Y)(P(X|Y)*log (P(X|Y)/P(X)) + (1- P(X|Y))*log ((1- P(X|Y)/(1-P(X)) • Sufficiency (X->Y) = P(X|Y)/P(X|!Y); Necessity (X->Y) = P(!X|Y)/P(!X|!Y). Interestingness of Y->X is NC++ = 1-N(X->Y)*P(Y), if N(…) is less than 1 or 0 otherwise

  30. Can We Find All and Only Interesting Patterns? • Find all the interesting patterns: Completeness • Can a data mining system find all the interesting patterns? • Association vs. classification vs. clustering • Search for only interesting patterns: Optimization • Can a data mining system find only the interesting patterns? • Approaches • First general all the patterns and then filter out the uninteresting ones. • Generate only the interesting patterns—mining query optimization

  31. A Multi-Dimensional View of Data Mining Classification • Databases to be mined • Relational, transactional, object-oriented, object-relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW, etc. • Knowledge to be mined • Characterization, discrimination, association, classification, clustering, trend, deviation and outlier analysis, etc. • Multiple/integrated functions and mining at multiple levels • Techniques utilized • Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, neural network, etc. • Applications adapted • Retail, telecommunication, banking, fraud analysis, DNA mining, stock market analysis, Web mining, Weblog analysis, etc.

  32. OLAP Mining: An Integration of Data Mining and Data Warehousing • Data mining systems, DBMS, Data warehouse systems coupling • No coupling, loose-coupling, semi-tight-coupling, tight-coupling • On-line analytical mining data • integration of mining and OLAP technologies • Interactive mining multi-level knowledge • Necessity of mining knowledge and patterns at different levels of abstraction by drilling/rolling, pivoting, slicing/dicing, etc. • Integration of multiple mining functions • Characterized classification, first clustering and then association

  33. An OLAM Architecture Mining query Mining result Layer4 User Interface User GUI API OLAM Engine OLAP Engine Layer3 OLAP/OLAM Data Cube API Layer2 MDDB MDDB Meta Data Database API Filtering&Integration Filtering Layer1 Data Repository Data cleaning Data Warehouse Databases Data integration

  34. Major Issues in Data Mining (1) • Mining methodology and user interaction • Mining different kinds of knowledge in databases • Interactive mining ofknowledge at multiple levels of abstraction • Incorporation of background knowledge • Data mining query languages and ad-hoc data mining • Expression and visualization of data mining results • Handling noise and incomplete data • Pattern evaluation: the interestingness problem • Performance and scalability • Efficiency and scalability of data mining algorithms • Parallel, distributed and incremental mining methods

  35. Major Issues in Data Mining (2) • Issues relating to the diversity of data types • Handling relational and complex types of data • Mining information from heterogeneous databases and global information systems (WWW) • Issues related to applications and social impacts • Application of discovered knowledge • Domain-specific data mining tools • Intelligent query answering • Process control and decision making • Integration of the discovered knowledge with existing knowledge: A knowledge fusion problem • Protection of data security, integrity, and privacy

  36. Summary • Data mining: discovering interesting patterns from large amounts of data • A natural evolution of database technology, in great demand, with wide applications • A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation • Mining can be performed in a variety of information repositories • Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc. • Classification of data mining systems • Major issues in data mining

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