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?
5 minutes for questions. 20 minutes total talk. Introduction 5 minutes. Neural Networks 5 min. Rule Extraction 5 min

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? Types (Supervised versus Unsupervised)
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. Simple Neural Networksy1
x0=1 (Bias)
Hidden Node Bias = 1 x1 x2 x3 y2 y3 y4
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: