1. Prepared by: Mahmoud Rafeek Al-Farra
College of Science & Technology
Dep. Of Computer Science & IT
BCs of Information Technology
Data Mining
Chapter 4_3: Classification Methods
(Examples)
Prepared by: Mahmoud Rafeek Al-Farra
2013

2. 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

3. Out Lines Machine learning techniques k-Nearest Neighbors
Naïve Bayesian Classifiers

4. k-Nearest Neighbors Also called instance based learning.
K-nearest neighbor is a supervised learning algorithm where the result of new instance query is classified based on majority of K-nearest neighbor category.
The purpose of this algorithm is to classify a new object based on attributes and training samples.

5. k-Nearest Neighbors
The classifiers do not use any model to fit and only based on memory.
Given a query point,
We find K number of objects or (training points) closest to the query point.
The classification is using majority vote among the classification of the K objects.

6. k-Nearest Neighbors - Algorithm
Given a new instance x,
find its nearest neighbor <x’,y’>
Return y’ as the class of x

7. Advantage and Disadvantage
Robust to noisy training data
Effective if the training data is large
Disadvantage
Need to determine value of parameter K
Distance based learning is not clear which type of distance to use and which attribute to use to produce the best results.
Computation cost is quite high because we need to compute distance of each query instance to all training samples.

8. Next …
Naïve Bayesian Classifiers
Artificial Neural Networks

9. Thanks