1. Dr. Russell Anderson Dr. Musa Jafar West Texas A&M University

2.  The process of discovering useful information in large data repositories. (Tan, P-N., Steinbach, M., and Kumar, V., Introduction to Data Mining, Addison-Wesley, 2006)  Discovered information should be:  Valid  Previously unknown  Actionable

3.  Seven objectives of Lenox and Cuff in 2002 (based on ACM 2001 Ironman Report)  Prepare and warehouse data  Process data based on set of DM algorithms  Analyze results  Make predictions  Select proper algorithm  Make application  Motivated to continue graduate studies in DM  We have added  Get to know data using statistical analysis tools  Use visualization tools for analysis and review

4. 1. Get to know the data. 2. Select an appropriate data mining algorithm based on the data and the mining objective. 3. Construct a model using the selected algorithm. 4. Analyze the results. 5. Make application.

5.  How is it structured?  Single table/flat-file.  Multi-table – relationships  Number of observations  Number of dimensions (attributes)  Compute summary statistics using tool such as MS-Excel  Visually evaluate characteristics of the data

6.  Tools developed:  Correlation Matrix  Scatter Plot  Parallel Coordinate Plot

7.  Distributions of data  Data ranges of numeric attributes  Cardinality of discrete attributes  Shape of distribution  Skewed  Multi-model  Location of outliers  Identification possible relationships between attributes  Identification of subpopulations within the data

8.  Microsoft Business Intelligence Tools  Association Analysis – aka market basket analysis  Classification  Decision Trees  Artificial Neural Network  Bayesian Analysis  Regression  Cluster Analysis  Custom Tools with Embedded Visual Presentation  Artificial neural network for both classification and regression  Self-Organizing Map (SOM) for cluster analysis

9.  Purpose of each methodology  Steps of underlying algorithm  Data types supported  Issues in construction and application  Parameter settings  Results interpretation

10.  Does the model fit the training data too well?  Need to separate available into training and validation subsets.  Visual view of training progress valuable.

11.  Mushroom edibility classifiers Classifier A Actual EdiblePoisonous PredictedEdible38%0% Poisonous8%54% Classifier B Actual EdiblePoisonous PredictedEdible44%1% Poisonous2%53%

12.  Black Box - models built using sophisticated methodologies (ANN’s for example) perform very well, but gaining an understanding of the model itself is difficult.  Contribution of individual input attributes  Nature of contribution (shape of curve)  Interaction between input attributes

13.  For a detailed presentation of the mechanics of the software deployed, attend our workshop tomorrow morning.  Saturday: 8-10 AM  Kachina A  Microsoft SQL Server Business Intelligence Studio  Visualization Tools