The document discusses supervised and unsupervised learning in data mining. It describes the main types of data mining as supervised, which includes techniques like Bayesian modeling and decision trees when the type and number of classes are known in advance, and unsupervised, which includes clustering techniques like one-way and two-way clustering when the types and number of classes are not known in advance. It provides examples of clustering, including min-max distance clustering to maximize inter-cluster distances and minimize intra-cluster distances, and k-means clustering to group similar data points into k number of clusters.

1. Data Warehousing
Lecture-31
Supervised vs. Unsupervised Learning
Virtual University of PakistanVirtual University of Pakistan
Ahsan Abdullah
Assoc. Prof. & Head
Center for Agro-Informatics Research
www.nu.edu.pk/cairindex.asp
National University of Computers & Emerging Sciences, Islamabad
Email: ahsan101@yahoo.com

2. Data Structures in Data Mining
• Data matrix
– Table or database
– n records and m attributes,
– n >> m
C1,1 C1,2 C1,3 C1,m
C2,1 C2,2 C2,3 C2,m
C3,1 C3,2 C3,3 C3,m
Cn,1 Cn,2 Cn,3 Cn,m
…
.
.
.
…
.
.
.
1 S1,2 S1,3 S1,n
S2,1 1 S2,3 S2,n
S3,1 S3,2 1 S3,n
Sn,1 Sn,2 Sn,3 1
…
.
.
.
…
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.
• Similarity matrix
– Symmetric square matrix
– n x n or m x m

3. Main types of DATA MINING
Supervised
• Bayesian Modeling
• Decision Trees
• Neural Networks
• Etc.
Unsupervised
• One-way Clustering
• Two-way Clustering
Type and number of
classes are NOT
known in advance
Type and number of
classes are known in
advance

4. Clustering: Min-Max Distance
Age
Salary
20 40 60
outlier Inter-cluster
distances are
maximized
Intra-cluster
distances are
minimized

5. How Clustering works?

6. One-way clustering example
INPUT OUTPUT
Black spots
are noise
White spots
are missing
data

7. Data Mining Agriculture data
INPUT Clustered OUTPUT
clusters

8. Which class?
Classifier (model)
Unseen Data
Classification

9. Output
Confidence
Level
Inputs
How Classification work?

10. Classification Process (1): Model Construction
TrainingTraining
DataData
NAME Time Items Gender
Moin 10 2 M
Munir 16 3 M
Meher 15 1 F
Javed 5 1 M
Mahin 20 1 F
Akram 20 4 M
ClassificationClassification
AlgorithmsAlgorithms
IF time/items >= 6
THEN gender = ‘F’
ClassifierClassifier
(Model)(Model)
(observations, measurements, etc.)
Relationship between shopping time and items bought

11. Classification Process (2): Use the Model in Prediction
TestingTesting
DataData Unseen DataUnseen Data
(Firdous, Time= 15 Items = 1)
ClassifierClassifier
Gender?
NAME Time Items Gender
Tahir 20 1 M
Younas 11 2 M
Yasin 3 1 M

12. Clustering vs. Cluster Detection

13. Clustering vs. Cluster Detection Example
AA BB

14. The K-Means Clustering

15. The K-Means Clustering: Example
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A B
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16. The K-Means Clustering: Comment