1. Prepared by: Mahmoud Rafeek Al-Farra
College of Science & Technology
Dep. Of Computer Science & IT
BCs of Information Technology
Data Mining
Chapter 6: Clustering Methods
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 Clustering Why clustering?
Where to use clustering?
Next: Types of Data in Cluster Analysis
Next: A Categorization of Major Clustering Methods

4. Definition of Clustering
Clustering can be considered the most important unsupervised learning technique; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data.
Clustering is “the process of organizing objects into groups whose members are similar in some way”.
A cluster is therefore a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters.

5. Definition of Clustering
Cluster: a collection of data objects
Similar to one another within the same cluster
Dissimilar to the objects in other clusters
Cluster analysis
Grouping a set of data objects into clusters
Clustering is unsupervised classification: no predefined classes

6. Learning

7. Why clustering? Simplifications Pattern detection
Useful in data concept construction
Unsupervised learning process

8. Where to use clustering?
Data mining
Information retrieval
text mining
Web analysis
marketing
medical diagnostic

9. Which method should I use?
Type of attributes in data
Scalability to larger dataset
Ability to work with irregular data
Time cost
complexity
Data order dependency
Result presentation

10. Thanks