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 purchases at department/ grocery stores Bank/Credit Card transactions Computers have become cheaper and more powerful Competitive Pressure is Strong Why Mine Data? Commercial Viewpoint
Provide better, customized services for an edge (e.g. in Customer Relationship Management)
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
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 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”
Much of the data is never analyzed at all
What is Data Mining? 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? 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,) 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 High dimensionality of data Origins of Data Mining Machine Learning/ Pattern Recognition Statistics/ AI Data Mining Database systems
Heterogeneous, distributed nature of data
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
Naïve Bayes and Bayesian Belief Networks
Illustrating Classification Task
Software Demonstrations SAS Enterprise Miner R Rattle Weka
SAS Enterprise Miner
Screenshot – EM Tutorial Workflow
R Rattle > source("http://www.ggobi.org/downloads/install.r")
> install(“rattle”, dep=TRUE)
R. Grossman, C. Kamath, V. Kumar, “Data Mining for Scientific and Engineering 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 http://rescomp.virginia.edu R 2.5.0 with Rattle (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