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Neural Networks in Data Mining “An Overview”

Neural Networks in Data Mining “An Overview”. Mahdi Nasereddin Ph.D. Pennsylvania State University School of Information Sciences and Technology. Agenda. Introduction Data Mining Techniques Neural Networks for Data Mining? Neural Networks Classification Neural Networks Prediction

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Neural Networks in Data Mining “An Overview”

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  1. Neural Networks in Data Mining “An Overview” Mahdi Nasereddin Ph.D. Pennsylvania State University School of Information Sciences and Technology

  2. Agenda • Introduction • Data Mining Techniques • Neural Networks for Data Mining? • Neural Networks Classification • Neural Networks Prediction • Conclusion • Questions?

  3. Introduction • Data Mining Definitions: • Building compact and understandable models incorporating the relationships between the description of a situation and a result concerning the situation. • Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases.

  4. Kinds of Data Mining Problems • Classification / Segmentation • Forecasting/Prediction (how much) • Association rule extraction (market basket analysis) • Sequence detection

  5. Data Mining Techniques: • Neural Networks • Decision Trees • Multivariate Adaptive Regression Splines (MARS) • Rule Induction • Nearest Neighbor Method and discriminant analysis • Genetic Algorithms • Boosting

  6. Neural Networks • What are they? • Based on early research aimed at representing the way the human brain works • Neural networks are composed of many processing units called neurons • Types (Supervised versus Unsupervised) • Training

  7. y1 x1 y2 x2 y3 x3 y4 Hidden NodeBias = 1 x0=1 (Bias) Simple Neural Networks Feed Forward Neural Network

  8. Neural Networks and Data Mining • Classification / Segmentation “LVQ, and Kohonen” • Forecasting/Prediction “BP, GRNN, and RBF” • Approximate Any Continuous function!!! “Hornik 1989” • Sequence detection “Recurrent Neural Networks”

  9. Neural Networks are great, but.. • Problem 1: The black box model! • Solution: 1. Do we really need to know? • Solution 2. Rule Extraction techniques • Problem 2: Long training times • Solution 1: Get a faster PC with lots of RAM • Solution 2: Use faster algorithms “For example: Quickprop” • Problems 3-: Back propagation • Solution: Evolutionary Neural Networks!

  10. Rule Extraction Techniques • Representation Methods • Extraction Strategy • Network Requirement

  11. Evolutionary Neural Networks • Using Genetic Algorithms to train the neural network • Why?

  12. Conclusions • Neural Networks in Data Mining? • Research opportunities • ENN • SVM

  13. Questions • Future questions:mxn16@psu.edu

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