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Prepared by: Mahmoud Rafeek Al-Farra College of Science & Technology Dep. Of Computer Science & IT BCs of Information Technology Data Mining Chapter 4_4: Classification Methods (Examples) Prepared by: Mahmoud Rafeek Al-Farra 2013 www.cst.ps/staff/mfarra

Course’s Out Lines Introduction Data Preparation and Preprocessing Data Representation Classification Methods Evaluation Clustering Methods Mid Exam Association Rules Knowledge Representation Special Case study : Document clustering Discussion of Case studies by students

Out Lines Naïve Bayesian Classifiers Artificial Neural Networks

Naïve Bayesian Classifiers A Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem (from Bayesian statistics) with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be "independent feature model".

What is ANN? An artificial neural network can be defined as a model of reasoning based on the human brain.

What is ANN? Analogy between biological and artificial neural networks

Artificial Neural Networks ANNs can be defined as a model of reasoning based on the human brain. A NN is a system of processing units, connections and weights associated with the connections which propagates activation from its input units to its output units, augmented by a learning rule.

Artificial Neural Networks ANN can be viewed as weighted directed graphs in which artificial neurons are nodes and directed edges labeled with weights are connections between neuron outputs and neuron inputs.

Simple computing element in ANN

Artificial Neural Networks The behavior of the network is determined by the combination of its architecture and its set of weights which is known as the learning of network.

Classification of ANN architectures

Feedforward neural network

Feedback neural network

Next … Evaluation

Thanks