1. Prepared by: Mahmoud Rafeek Al-Farra
College of Science & Technology
Dep. Of Computer Science & IT
BCs of Information Technology
Data Mining
Chapter 4: Classification Methods
(Definition)
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 Definition of Classification Learning
Supervised vs Unsupervised
Classification vs. Prediction
How does the classification work ?!

4. Definition of Classification
Classification is (Techniques used to predict group membership for data instances).
For example, you may wish to use classification to predict whether the weather on a particular day will be “sunny”, “rainy” or “cloudy”.

5. Definition of Classification
Classification is a classic data mining task, with roots in machine learning.
A typical application is : "Given past records of customers who switched to another supplier, predict which current customers are likely to do the same."

6. Classes are pre-defined
Learning
This type of learning called supervised learning
Example: Classification model may be built to categorize bank loan applications as either safe or risky.
Classes are pre-defined

7. Supervised vs Unsupervised
Supervised learning (classification)
The set of possible classes is known in advance.
New data is classified based on the training set
Unsupervised learning (clustering)
Set of possible classes is not known.
After classification we can try to assign a name to that class.
Unsupervised classification is called clustering.

8. Classification vs. Prediction
predicts categorical class labels (discrete or nominal)
classifies data based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data
Prediction:
models continuous-valued functions, i.e., predicts unknown or missing values

9. How ?! Given a collection of records (training set )
Each record contains a set of attributes, one of the attributes is the class.
Find a model for class attribute as a function of the values of other attributes.
Goal: previously unseen records should be assigned a class as accurately as possible.
A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.

10. How ?! Example: Process 1 Model Construction Classification Algorithms
Training
Data
Classifier
(Model)
IF rank = ‘professor’
OR years > 6
THEN tenured = ‘yes’

11. How ?! Tenured? Process 2 Using the Model in Prediction Classifier
Testing
Data
Unseen Data
(Jeff, Professor, 4)
Tenured?

12. How ?! 12

13. Next … Machine learning techniques Decision Trees Neural Networks
k-Nearest Neighbors
Naïve Bayesian Classifiers

14. Thanks