Datamining
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Datamining

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Datamining Datamining Presentation Transcript

  • Data Mining at UVA New Horizons in Teaching and Learning Conference May 21-24, 2007 Kathy Gerber, ITC Research Computing [email_address]
    • Lots of data is being collected and warehoused
      • Web data, e-commerce
      • 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)
    Why Mine Data? Commercial Viewpoint
  • 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 ( e.g., GEOSS )
      • scientific simulations generating terabytes of data
    • Traditional techniques infeasible for raw data
    • Data mining may help scientists
      • in classifying and segmenting data
      • in Hypothesis Formation
  • Mining 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
    The Data Gap Total new disk (TB) since 1995 Number of analysts From: R. Grossman, C. Kamath, V. Kumar, “Data Mining for Scientific and Engineering Applications”
  • What is Data Mining?
    • Many Definitions
      • Non-trivial extraction of implicit, previously unknown and potentially useful information from data
      • Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns
  • Summary of SAS DM Process - SEMMA
    • Sample the data by creating one or more data tables. The sample should be large enough to contain the significant information, yet small enough to process.
    • Explore the data by searching for anticipated relationships, unanticipated trends, and anomalies in order to gain understanding and ideas.
    • Modify the data by creating, selecting, and transforming the variables to focus the model selection process.
    • Model the data by using the analytical tools to search for a combination of the data that reliably predicts a desired outcome.
    • Assess the data by evaluating the usefulness and reliability of the findings from the data mining process.
  • What is (not) Data Mining?
    • What is Data Mining?
      • Certain names are more prevalent in certain US locations (O’Brien, O’Rurke, O’Reilly… in Boston area)
      • Group together similar documents returned by search engine according to their context (e.g. Amazon rainforest, Amazon.com,)
    • What is not Data Mining?
      • Look up phone number in phone directory
      • Query a Web search engine for information about “Amazon”
    • Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems
    • Traditional Techniques may be unsuitable due to
      • Enormity of data
      • High dimensionality of data
      • Heterogeneous, distributed nature of data
    Origins of Data Mining Machine Learning/ Pattern Recognition Statistics/ AI Data Mining Database systems
  • 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.
  • Examples of Classification Task
    • Predicting tumor cells as benign or malignant
    • Classifying credit card transactions as legitimate or fraudulent
    • Classifying secondary structures of protein as alpha-helix, beta-sheet, or random coil
    • Categorizing news stories as finance, weather, entertainment, sports, etc
  • Classification Techniques
    • Decision Tree based Methods
    • Rule-based Methods
    • Memory based reasoning
    • Neural Networks
    • Naïve Bayes and Bayesian Belief Networks
    • Support Vector Machines
  • Illustrating Classification Task
  • Software Demonstrations SAS Enterprise Miner R Rattle Weka
  • SAS Enterprise Miner
  • Screenshot – EM Tutorial Workflow
  • R Rattle
    • Install R 2.5.0   
    • > source("http://www.ggobi.org/downloads/install.r")
    • > install(“rattle”, dep=TRUE)
  • Weka
    • Slide Credits
    • R. Grossman, C. Kamath, V. Kumar, “Data Mining for Scientific and Engineering
    • Applications”
    • SAS Enterprise Miner tutorial
    • Frank Eibe, Machine Learning with Weka
    • Tan, Steinbach, Kumar “Introduction to Data Mining”
  • Versions and References for Software Used Today
    • SAS 9.1.3 EAS with Enterprise Miner
      • UVA licensed software
      • http://rescomp.virginia.edu
    • R 2.5.0 with Rattle (open source)
      • Open source
    • Weka (open source)
      • Ian Witten, Frank Eibe: Data Mining: Practical Machine Learning Tools and Techniques (Second Edition)
    • Not demonstrated but also see Insightful Miner and Orange