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Data Mining Xuequn Shang NorthWestern Polytechnical University


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  • 1. Data Mining Xuequn Shang NorthWestern Polytechnical University September 2006
  • 2. About the Course
    • Time
      • Tue. 7:00 pm ~9:00 pm
      • Fri. 7:00 pm~9:00 pm
    • Location
      • Room XA107 West building
    • Instructor
      • Xuequn shang, Ph.D.
      • [email_address]
  • 3. Mini Survey
    • How many people took database course before?
    • How many people took statistic course?
    • How many people took machine learning before?
  • 4. Textbook and Reference
    • Text book
      • Data Mining: Concepts and Techniques, JiaweiHan and Micheline Kamber, Morgan Kaufmann, 2001.
      • 范明、孟小峰等译,数据挖掘概念与技术,机械工业出版社, 2001 年 8 月
    • References
      • Principles of Data Mining (Adaptive Computation and Machine Learning), David J. Hand, Heikki Mannila, Padhraic Smyth, MIT Press, 2001
      • Many research papers
  • 5. Course Introduction
    • Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances.
      • Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines.
      • Such data is often stored in data warehouses and data marts specifically intended for management decision support.
    • Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories.
      • Such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments.
    • This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to-use software and cases.
  • 6. Course Objective
    • To provide an introduction to knowledge discovery in databases and complex data repositories, and to present basic concepts relevant to real data mining applications, as well as reveal important research issues germane to the knowledge discovery domain and advanced mining applications.
    • Students will understand the fundamental concepts underlying knowledge discovery in databases and gain hands-on experience with implementation of some data mining algorithms applied to real world cases.
  • 7. Evaluation
    • Assignments (2) 20%
    • Class participant 10%
    • Project 20%
    • Final Exam 50%
    • – Quality of presentation + quality of report + quality of demos
  • 8. About the Project
    • Implement and experimentally evaluate the major method in the paper (60%)
    • If possible, improve the method in effectiveness or efficiency, implement and experimentally evaluate your improvement
    • Write a technical report (40%)
  • 9. Contents
    • Introduction to Data Mining
    • Association analysis
    • Sequential Pattern Mining
    • Classification and prediction
    • Data Clustering
    • Data preprocessing
    • Advanced topics
  • 10. Course Schedule(1) Session 6 Oct- 13 classification Session 5 Oct- 10 Sequential Pattern Mining Session 4 Sep- 29 Session 3 Sep- 26 Association rule mining Session 2 7:00 pm-9:00 pm Sep- 22 Welcome and introduction Session 1 7:00 pm-9:00 pm Sep- 19 Topic Session Time Date
  • 11. Course Schedule(2) Seminar Session 12 Nov- 3 Session 11 Oct- 31 Advance topic Session 10 Oct- 27 Session 9 Oct- 24 Data preprocessing Session 8 Oct- 20 Data Clustering Session 7 Oct- 17 Topic Session Time Date
  • 12. Course Schedule(3) Session 8 Nov- 10 examination Session 7 Nov- 7 Topic Session Time Date
  • 13. Useful Information
    • How to get a paper online?
      • DBLP
        • A good index for good papers
      • CiteSeer
      • Just google it
      • Send requests to the authors
    • Conferences and Journals on Data Mining
      • KDD, PAKDD, ICDM, DAWAK, PKDD, etc.
      • DMKD, TKDE, ACM Trans. on KDD. etc.
  • 14. Additional Hits
    • Be a good citizen
    • Be a good graduate student
    • Be a good scientist
      • There are three chief ethical problems: frauds, plagiarism, and duplicate or simultaneous submissions
      • There are four basic considerations in technical ethics: honesty, justice, respect for other’s works and copyrights held by others.
  • 15. Introduction
    • Why data mining?
    • What is data mining?
    • What kind of data to be mined?
    • Are all the patterns interesting?
    • Data mining functionality
    • Major issues in data mining
  • 16. Why Data Mining?
    • Changes in the Business Environment
      • Customers becoming more demanding
      • Markets are saturated
    • Databases today are huge:
      • More than 1,000,000 entities/records/rows
      • From 10 to 10,000 fields/attributes/variables
      • Gigabytes and terabytes
    • Databases a growing at an unprecedented rate
    • Decisions must be made rapidly
    • Decisions must be made with maximum knowledge
    • We are drowning in data, but starving for knowledge!
    • “ Necessity is the mother of invention ” — Data mining — Automated analysis of massive data sets
  • 17. Why Data Mining?
    • “ The key in business is to know something that nobody else knows.”
    • — Aristotle Onassis
    • “ To understand is to perceive patterns.”
    • — Sir Isaiah Berlin
  • 18. What Is Data Mining?
    • Mining data –extracting or mining knowledge from large amount of data
    • Data mining
      • is the non-trivial process of identifying valid , novel , potentially useful , and ultimately understandable patterns in data [Fayyad, Piatetsky-Shapiro, Smyth, 96]
  • 19. Applications
    • Data analysis and decision support
      • Market analysis and management
        • Target marketing, customer relationship management (CRM), market basket analysis, cross selling, market segmentation
      • Risk analysis and management
        • Forecasting, customer retention, improved underwriting, quality control, competitive analysis
      • Fraud detection and detection of unusual patterns (outliers)
    • Other Applications
      • Text mining (news group, email, documents) and Web mining
      • Stream data mining
      • Bioinformatics and bio-data analysis
  • 20. Ex. 1: Market Analysis and Management
    • Where does the data come from?—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
    • Cross-market analysis—Find associations/co-relations between product sales, & predict based on such association
    • Customer profiling—What types of customers buy what products (clustering or classification)
    • Customer requirement analysis
      • Identify the best products for different customers
      • Predict what factors will attract new customers
    • Provision of summary information
      • Multidimensional summary reports
      • Statistical summary information (data central tendency and variation)
  • 21. Ex. 2: Corporate Analysis & 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
  • 22. Ex. 3: Fraud Detection & Mining Unusual Patterns
    • Approaches: Clustering & model construction for frauds, outlier analysis
    • Applications: Health care, retail, credit card service, telecomm.
      • Auto insurance : ring of collisions
      • Money laundering: suspicious monetary transactions
      • Medical insurance
        • Professional patients, ring of doctors, and ring of references
        • Unnecessary or correlated screening tests
      • Telecommunications: phone-call fraud
        • Phone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm
      • Retail industry
        • Analysts estimate that 38% of retail shrink is due to dishonest employees
      • Anti-terrorism
  • 23. The KDD Process
      • Data mining—core of knowledge discovery process
    Data Cleaning Data Integration Databases Data Warehouse Knowledge Task-relevant Data Selection Data Mining Pattern Evaluation
  • 24.
    • Preprocessing
      • Data cleaning
      • Data integration
    • Data selection
    • Data transformation
    • Data mining
    • Pattern evaluation
    • Knowledge presentation
    KDD Process Steps
  • 25. Confluence of Multiple Disciplines Data Mining Database Technology Statistics Machine Learning Pattern Recognition Algorithm Other Disciplines Visualization
  • 26. Classification Schemes
    • General functionality
      • Descriptive data mining
      • Predictive data mining
    • Different views lead to different classifications
      • Data view: Kinds of data to be mined
      • Knowledge view: Kinds of knowledge to be discovered
      • Method view: Kinds of techniques utilized
      • Application view: Kinds of applications adapted
  • 27. What Kind of Data?
    • Database-oriented data sets and applications
      • Relational database, data warehouse, transactional database
    • Advanced data sets and advanced applications
      • Data streams and sensor data
      • Time-series data, temporal data, sequence data (incl. bio-sequences)
      • Structure data, graphs, social networks and multi-linked data
      • Object-relational databases
      • Heterogeneous databases and legacy databases
      • Spatial data and spatiotemporal data
      • Multimedia database
      • Text databases
      • The World-Wide Web
  • 28.
    • Structured data
      • Table –records –attributes
      • Indexes & SQL
    • Online transactional processing (OLTP)
      • Insert a student “Jennet” into class CMPT 741, fall 2005
    • Online analytical processing (OLAP)
      • Find the average class size of CMPT 700 level courses in the last 3 years, grouped by semesters
    Relational Databases
  • 29.
    • A subject-oriented , integrated , time-variant , and nonvolatile collection of data in support of management’s decision making process [Inmon]
    Data Warehouses Data Warehouse Clean Transform Integrate Load Query and analysis tools Client Client
  • 30.
    • A Multi-dimensional Database
    Data Cube A B 29 30 31 32 1 2 3 4 5 9 13 14 15 16 64 63 62 61 48 47 46 45 a1 a0 c3 c2 c1 c 0 b3 b2 b1 b0 a2 a3 C B 44 28 56 40 24 52 36 20 60
  • 31. Transactional Databases What kind of product combinations that customers like to buy together? … … Beer, cook, fish, potato, orange, apple T200 Milk, bread, beer, diaper T100 Itemset TID
  • 32.
    • Spatial information
      • Geographic databases (map)
      • VLSI chip design databases
      • Satellite image databases
    • Spatial patterns
      • What are the changes of the forest in the last 10 years?
      • Find clusters of homes with kids of age 5-10
    Spatial Databases
  • 33.
    • A sequence of values that change over time
      • The sequences of stock price at every 5 minutes
      • The daily temperature
    • Typical operations
      • Similarity search
      • Trend analysis
    Time Series Data
  • 34.
    • HTML web documents
    • XML documents
    • Digital libraries
    • Annotated multimedia databases
      • Image, audio and video data
    Semi-Structure Data
  • 35.
    • Bio-sequences
      • DNA, gene, protein: very long sequences
    • Micro-array data
    • Medical documents and images
    • Typically very noisy
      • Data cleaning and integration are challenging
    Biological Data
  • 36.
    • What can be discovered depends upon the data mining task employed.
    • Descriptive DM tasks
      • characterize general properties
    • Predictive DM tasks
      • Infer on available data
    What Can Be Discovered?
  • 37. What Kinds of Patterns?
    • Association rules and sequential patterns
    • Classification
    • Clustering
    • Outlier analysis
    • Other data mining tasks
  • 38. Are All the “Discovered” Patterns Interesting?
    • Data mining may generate thousands even million of patterns: Not all of them are interesting
      • What makes a pattern interesting?
      • Can a data mining system generate all of the interesting patterns?
      • Can a data mining system generate only interesting patterns?
  • 39. What makes a pattern interesting?
    • 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, etc.
  • 40. Find All Interesting Patterns?
    • Find all the interesting patterns: Completeness
      • Can a data mining system find all the interesting patterns? Do we need to find all of the interesting patterns?
      • Heuristic vs. exhaustive search
      • Association vs. classification vs. clustering
  • 41. Find Only Interesting Patterns?
    • Search for only interesting patterns: An optimization problem
      • 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
  • 42.
    • Effectiveness
    • Efficiency
    • Applications
    • Theory
    Research Issues in Data Mining
  • 43.
    • What kind of patterns to mine?
      • Propose interesting data mining problems
    • How to identify interesting patterns
      • Interestingness measures
      • Useful constraints
    • Visualization and interaction
      • Presentation of mining results
      • Interactive, adaptive mining
  • 44.
    • Develop fast data mining algorithms
      • Identify effective heuristics for mining
      • Theoretical and/or empirical justification
    • Systematic implementation
      • Parallel, distributed, and incremental mining
    • Integration to product systems
      • Data mining module in DBMS and data warehouses
  • 45.
    • Handle noisy or incomplete data
    • Incorporate background knowledge
    • Application/domain-oriented solutions
      • Vertical solutions
  • 46.
    • Knowledge representation
    • Data mining algebra and language
      • Integration of multiple mining tasks/DBMS
      • Open for new data/knowledge
      • Interaction and visualization
    • Data mining query optimization
      • Common construct
      • Automatic optimization by construct rewriting
    Foundation for Data Mining
  • 47. Major Issues in Data Mining
    • Mining methodology
      • Mining different kinds of knowledge from diverse data types, e.g., bio, stream, Web
      • Performance: efficiency, effectiveness, and scalability
      • Pattern evaluation: the interestingness problem
      • Incorporation of background knowledge
      • Handling noise and incomplete data
      • Parallel, distributed and incremental mining methods
      • Integration of the discovered knowledge with existing one: knowledge fusion
    • User interaction
      • Data mining query languages and ad-hoc mining
      • Expression and visualization of data mining results
      • Interactive mining of knowledge at multiple levels of abstraction
    • Applications and social impacts
      • Domain-specific data mining & invisible data mining
      • Protection of data security, integrity, and privacy
  • 48. A Brief History of Data Mining Society
    • 1989 IJCAI Workshop on Knowledge Discovery in Databases
      • Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991)
    • 1991-1994 Workshops on Knowledge Discovery in Databases
      • Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 1996)
    • 1995-1998 International Conferences on Knowledge Discovery in Databases and Data Mining (KDD’95-98)
      • Journal of Data Mining and Knowledge Discovery (1997)
    • ACM SIGKDD conferences since 1998 and SIGKDD Explorations
    • More conferences on data mining
      • PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), etc.
    • ACM Transactions on KDD starting in 2007
  • 49. 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.
    • Data mining systems and architectures
    • Major issues in data mining
  • 50. Assignment (Ⅰ)
    • What is data mining?
      • Data mining is the task of discovering interesting patterns from large amounts of data , where the data can be stored in databases, data warehouses, or other information repositories . It is a young interdisciplinary field , drawing from areas such as database systems, data warehousing, statistics, machine learning, data visualization, information retrieval, and high-performance computing. Other contributing areas include neural networks, pattern recognition, spatial data analysis, image databases, signal processing, and many application fields, such as business, economics, and bioinformatics.
  • 51. Assignment (Ⅱ)
    • Define each of the following data mining functionalities: association and correlation analysis, classification, prediction, clustering, and evolution analysis. Give example of each data mining functionality, using a real-life database with which you are familiar.
      • Association analysis
        • showing attribute-value conditions that occur frequently in a given set of data
      • Classification
        • finding a set of models that describe and distinguish data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown
      • Clustering analysis
        • analyzing data objects without consulting a known class label
      • Outlier analysis
        • finding data objects that do not comply with the general behavior or model of the data
      • Evolution analysis
        • describes and models regularities or trends for objects whose behavior changes over time
  • 52. Complement (Ⅰ)
    • A student asked me what the difference between data mining and information retrieval is
      • There is really no clear difference
      • Actually some of the recent information retrieval system do discover associations between words and paragraphs
  • 53. Complement (Ⅱ)
    • What is the difference between data mining (DM) and pattern recognition (PR)
      • Both of them are to find useful relations
      • In PR, we typically deal with data set of moderate size, while in a typical DM application, we are concerned with data sets that are large in terms of dimension and number of clusters
      • PR is an important techniques used in DM
    Data mining involves an integration of techniques from multiple disciplines
  • 54. Architecture: Typical Data Mining System data cleaning, integration, and selection Database or Data Warehouse Server Data Mining Engine Pattern Evaluation Graphical User Interface Knowledge-Base Database Data Warehouse World-Wide Web Other Info Repositories
  • 55. Thank you !