Data Mining: Implementation of Data Mining Techniques using RapidMiner software presentation

1. Data Mining:
Implementation of Data
Mining Techniques using
RapidMiner software
Prepared by
Mohammed Kharma

2. Definitions review
• Cluster: A collection of data objects
– similar (or related) to one another within the
same group
– dissimilar (or unrelated) to the objects in other
groups
• Cluster analysis
– Finding similarities between data according to the
characteristics found in the data and grouping
similar data objects into clusters

3. Clustering Methods
• Partitioning :
– Unsupervised learning algorithms, Construct various
partitions and then evaluate them by some criterion,
e.g., minimizing the sum of square errors
– Typical methods: k-means, k-medoids
• Hierarchical :
– Create a hierarchical decomposition of the set of data
(or objects) using some criterion
– Typical methods: Diana, Agnes, BIRCH, ROCK,
CAMELEON

4. Illustration & compression of 2
clustering technique using
Rapidminer tool and Java
application

5. illustrate of 2 clustering technique
using Rapidminer tool and Java
• K-means algorithm:
We performed two test
1. Using java program: program parameters
K = 2;
Data:
22 21
19 20
18 22
1 3
3 2

6. 6
K-means Clustering
• Input: the number of clusters K and the collection of n
instances
• Output: a set of k clusters that minimizes the squared error
criterion
• Method:
– Arbitrarily choose k instances as the initial cluster centers
– Repeat
• (Re)assign each instance to the cluster to which the
instance is the most similar, based on the mean value of
the instances in the cluster
• Update cluster means (compute mean value of the
instances for each cluster)
– Until no change in the assignment
• Squared Error Criterion
– E = ∑i=1 k ∑ pЄCi |p-mi|2
– where mi are the cluster means and p are points in clusters

7. The result K-Means-java program

8. The result of K-Means-RapidMiner

9. The result of K-Means-RapidMiner

10. Continued-The result of K-Means-
RapidMiner

11. 11
K-medoids
• Input: the number of clusters K and the collection of n
instances
• Output: A set of k clusters that minimizes the sum of the
dissimilarities of all the instances to their nearest medoids
• Method:
– Arbitrarily choose k instances as the initial medoids
– Repeat
• (Re)assign each remaining instance to the cluster with
the nearest medoid
• Randomly select a non-medoid instance, or
• Compute the total cost, S, of swapping Oj with Or
• If S<0 then swap Oj with Or to form the new set of k
medoids
– Until no change

12. The result of k-medoids-RapidMiner

13. The result of k-medoids-RapidMiner

14. Java Live Demo:
http://home.dei.polimi.it/matteucc/Clustering/t
utorial_html/AppletKM.html

15. Comparison
The results of both algorithms are the same
Both require K to be specified in the
input
K-medoids is less influenced by outliers in the
data
Both methods assign each instance exactly to
one cluster

16. »Thank you