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
…
.
.
.
…
.
.
.
• 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
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