Neural Networks in Data Mining “An Overview”

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Presentation transcript:

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

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.

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

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

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

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

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”

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!

Rule Extraction Techniques Representation Methods Extraction Strategy Network Requirement

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

Conclusions Neural Networks in Data Mining? Research opportunities ENN SVM

Questions Future questions: mxn16@psu.edu