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Neural Networks in Data
Mining “An Overview”
Mahdi Nasereddin Ph.D.
Pennsylvania State University
School of Information Sciences
and Technology
1
Agenda
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
Introduction
Data Mining Techniques
Neural Networks for Data Mining?
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Neural Networks Classification
Neural Networks Prediction
Conclusion
Questions?
2
Introduction

Data Mining Definitions:
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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.
3
Kinds of Data Mining Problems
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Classification / Segmentation
Forecasting/Prediction (how much)
Association rule extraction (market basket
analysis)
Sequence detection
4
Data Mining Techniques:
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Neural Networks
Decision Trees
Multivariate Adaptive Regression Splines
(MARS)
Rule Induction
Nearest Neighbor Method and discriminant
analysis
Genetic Algorithms
Boosting
5
Neural Networks

What are they?
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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
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Simple Neural Networks
y1
x1
y2
x2
y3
x3
y4
x0=1 (Bias)
Hidden Node Bias = 1
Feed Forward Neural Network
7
Neural Networks and Data
Mining
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Classification / Segmentation “LVQ, and
Kohonen”
Forecasting/Prediction “BP, GRNN, and RBF”


Approximate Any Continuous function!!! “Hornik
1989”
Sequence detection “Recurrent Neural
Networks”
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Neural Networks are great,
but..
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Problem 1: The black box model!
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Problem 2: Long training times
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Solution: 1. Do we really need to know?
Solution 2. Rule Extraction techniques
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!
9
Rule Extraction Techniques
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Representation Methods
Extraction Strategy
Network Requirement
10
Evolutionary Neural Networks
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Using Genetic Algorithms to train the neural
network
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Why?
11
Conclusions
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
Neural Networks in Data Mining?
Research opportunities

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ENN
SVM
12
Questions

Future questions:
[email protected]
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