Download Data Mining

CLUSTERING
(Segmentation)
Saed Sayad
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Data Mining Steps
1
• Problem Definition
2
• Data Preparation
3
• Data Exploration
4
• Modeling
5
• Evaluation
6
• Deployment
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What is Clustering?
Given a set of records, organize
the records into clusters
Income
A cluster is a subset
of records which
are similar
Age
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Clustering Requirements
• The ability to discover some or all of the hidden
clusters.
• Within-cluster similarity and between-cluster
disimilarity.
• Ability to deal with various types of attributes.
• Can deal with noise and outliers.
• Can handle high dimensionality.
• Scalability, Interpretability and usability.
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Similarity - Distance Measure
To measure similarity or dissimilarity between
objects, we need a distance measure. The
usual axioms for a distance measure D are:
 D(x, x) = 0
 D(x, y) = D(y, x)
 D(x, y) ≤ D(x, z) + D(z, y) the triangle inequality
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Similarity - Distance Measure
k
 x  y 
Euclidean
2
i
i 1
k
Manhattan
x
i
i 1
Minkowski




i
 yi
 x  y 
k
i 1
q
i
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i
1q




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Similarity - Correlation
rxy 
 ( x  x )( y  y )
 ( x  x )  ( y  y)
i
i
2
i
Similar
2
i
Dissimilar
Credit$
Credit$
Age
Age
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Similarity – Hamming Distance
k
DH   xi  yi
i 1
Gene 1
A
A
T
C
C
A
G
T
Gene 2
T
C
T
C
A
A
G
C
Hamming Distance
1
1
0
0
1
0
0
1
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Clustering Methods
•
•
•
•
Exclusive vs. Overlapping
Hierarchical vs. Partitive
Deterministic vs. Probabilistic
Incremental vs. Batch learning
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Exclusive vs. Overlapping
Income
Income
Age
Age
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Hierarchical vs. Partitive
Income
Age
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Hierarchical Clustering
• Hierarchical clustering involves creating
clusters that have a predetermined ordering
from top to bottom. For example, all files and
folders on the hard disk are organized in a
hierarchy.
• There are two types of hierarchical clustering:
– Agglomerative
– Divisive
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Hierarchical Clustering
Agglomerative
Divisive
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Hierarchical Clustering - Agglomerative
1. Assign each observation to its own cluster.
2. Compute the similarity (e.g., distance)
between each of the clusters.
3. Join the two most similar clusters.
4. Repeat steps 2 and 3 until there is only a
single cluster left.
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Hierarchical Clustering - Divisive
1. Assign all of the observations to a single
cluster.
2. Partition the cluster to two least similar
clusters.
3. Proceed recursively on each cluster until
there is one cluster for each observation.
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Hierarchical Clustering – Single Linkage
r
s
L(r , s )  min( D( xri , xsj ))
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Hierarchical Clustering – Complete
Linkage
r
s
L(r , s)  max( D( xri , xsj ))
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Hierarchical Clustering – Average Linkage
r
s
1
L( r , s ) 
nr ns
nr
ns
 D( x
i 1 j 1
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ri
, xsj )
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K Means Clustering
1. Clusters the data into k groups where k is
predefined.
2. Select k points at random as cluster centers.
3. Assign observations to their closest cluster center
according to the Euclidean distance function.
4. Calculate the centroid or mean of all instances in
each cluster (this is the mean part)
5. Repeat steps 2, 3 and 4 until the same points are
assigned to each cluster in consecutive rounds.
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K Means Clustering
Income
Age
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K Means Clustering
Sum of Squares function
K
J    ( xn   j )
2
j 1 nS j
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Clustering Evaluation
•
•
•
•
•
Sarle’s Cubic Clustering Criterion
The Pseudo-F Statistic
The Pseudo-T2 Statistic
Beale’s F-Type Statistic
Target-based
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Clustering Evaluation
Chi2 Test
Categorical
K-S Test
Target
Variable
ANOVA
Numerical
H Test
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Chi2 Test
Actual
Predicted
Y
Y
n11
N
n12
N
n21
n22
r
c
  
2
(nij  eij )
i 1 j 1
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2
eij
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Analysis of Variance (ANOVA)
Source of
Variation
Sum of
Squares
Degree of
Freedom
Mean Square
F
P
Between
Groups
SSB
dfB
MSB = SSB/dfB
F=MSB/MSW
P(F)
Within Groups
SSW
dfw
MSW = SSW/dfw
Total
SST
dfT
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Clustering - Applications
• Marketing: finding groups of customers with similar
behavior.
• Insurance & Banking: identifying frauds.
• Biology: classification of plants and animals given their
features.
• Libraries: book ordering.
• City-planning: identifying groups of houses according
to their house type, value and geographical location.
• World Wide Web: document classification; clustering
weblog data to discover groups with similar access
patterns.
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Summary
• Clustering is the process of organizing objects
(records or variables) into groups whose
members are similar in some way.
• Hierarchical and K-Means are the two most
used clustering techniques.
• The effectiveness of the clustering method
depends on the similarity function.
• The result of the clustering algorithm can be
interpreted and evaluated in different ways.
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Questions?
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