1. MIS 451 Building Business Intelligence Systems
Clustering (2)

2. Problem Target Marketing Diaper, Baby food, Swiss cheese and Belgian
Toys chocolate
French Wine

3. Clustering
Clustering is a data mining method for grouping objects such that objects within the same cluster are similar and objects in different clusters are dissimilar.
Why clustering
SQL based OLAP is not suitable for clustering objects whose attributes have a large number of possible values
SQL based OLAP is not suitable for clustering objects with a large number of attributes

4. Clustering Steps in clustering objects
Compute similarity between objects
Clustering based on similarity between objects

5. Similarity
An object (e.g., a customer) has a list of variables (e.g., attributes of a customer such as age, spending, gender etc.)
When measuring similarity between objects we measure similarity between variables of objects.
Instead of measuring similarity between variables, we use distance to measure dissimilarity between variables.

6. Dissimilarity Manhattan distance Euclidean distance
Continuous variable
Manhattan distance
Euclidean distance

7. Dissimilarity
For two objects X and Y with continuous variables 1,2,…n, Manhattan distance is defined as:

8. Dissimilarity Example of Manhattan distance NAME AGE SPENDING($)
Sue
Carl
TOM
JACK

9. Dissimilarity
For two objects X and Y with continuous variables 1,2,…n, Euclidean distance is defined as:

10. Dissimilarity Example of Euclidean distance NAME AGE SPENDING($)
Sue
Carl
TOM
JACK

11. Dissimilarity Standardize values of an variable Calculate mean value
Calculate mean absolute deviation
Standardize values of an variable using the formula:
new value = (old value – mean value)/mean standard deviation

12. Dissimilarity Binary variable
distance = number of matched variables/total number of variables
NAME Married(Y/N) Gender Internet connection at home
Sue Y M Y
Carl Y F Y
TOM N M N
JACK N F N

13. Clustering based on dissimilarity
After calculating dissimilarity between objects, a dissimilarity matrix can be created with objects as indexes and dissimilarities between objects as elements.

14. Clustering based on dissimilarity
Sue Tom Carl Jack Mary
Sue
Tom
Carl
Jack
Mary

15. Clustering based on dissimilarity
Step 1:Initially, place each object in an unique cluster
Step 2: Calculate dissimilarity between clusters
Dissimilarity between clusters is the minimum dissimilarity between two objects of the clusters, one from each cluster
Step 3: Merge two clusters with the least dissimilarity
Step 4: Continue step 1-3 until all objects are in one cluster