Transcript Clustering - Courses - University of California, Berkeley

Clustering
Ram Akella
Lecture 6
February 23, 2011
1290& 280I
University of California Berkeley
Silicon Valley Center/SC
Class Outline
 Clustering Introduction
 Mechanics
 Recommender Systems
What is Cluster Analysis?
 Finding groups of objects such that the
objects in a group will be similar (or
related) to one another and different from
(or unrelated to) the objects in other
Inter-cluster
groups
Intra-cluster
distances are
distances are
minimized
maximized
Potential Applications
 Numerical Taxonomy
 Differentiation between different species
of animals or plants according to their
physical similarities
 Fifty plants from3 different species were
collected and some measurements were
taken such as: sepal length, sepal width,
petal length and petal width.
Potential Applications
 Market Segmentation
 The market is divided into smaller
groups that are more homogeneous
needs and wants of the market place
Objectives of cluster analysis
 Address the heterogenity in the data
 Divide the data into more homogeneous groups
 Find the natural modality of data
 Determine whether the data contains naturally
occurring, homogeneous subsets
Measures of distance, dissimilarity and density
 To measure of proximity of data we
can use
 Direct assessment of proximity
 Derived measure of proximity or
similarity
Distance Measurements
Euclidean Distance

2
d ij   ( xik  x jk ) 
 k

1/ 2
This is usually applied to standardized
data to give the same weight to all the
metrics (except when the input is the
Principal Components)
Distance Measurements
Minkowski Metric
p

dij   xik  x jk 
k

 The Euclidean distance is a especial case when
p=2
 When p=1 it is called city-block metric because it
is like walking from point A to point B in a city laid
out with a grid of streets
Mahalanobis Distance
This distance takes into account the covariance patterns of the
data
Dij2  ( xi  x j )'1 ( xi  x j )
Where Σ is the population covariance matrix of the data matrix X
The Mahalanobis distance captures the fact that a point A and a
point B are equally likely to have been drawn from a multivariate
normal distribution
Agglomerative Clustering
 Start with the points as individual clusters
 At each step, merge the closest pair of clusters
until only one cluster (or k clusters) left
 More popular hierarchical clustering technique

 Key operation is the computation of the proximity
of two clusters
 Different approaches to defining the distance
between clusters distinguish the different algorithms
Agglomerative Clustering
Algorithm
Basic algorithm is :
1.
2.
3.
4.
5.
6.
Compute the proximity matrix
Let each data point be a cluster
Repeat
Merge the two closest clusters
Update the proximity matrix
Until only a single cluster remains
Starting Situation
Start with clusters of individual points and a proximity
p1 p2
p3
p4 p5
...
matrix
p1
p2
p3
p4
p5
.
.
.
Proximity Matrix
...
p1
p2
p3
p4
p9
p10
p11
p12
Intermediate Situation
After some merging steps, we have some clusters
C1
C2
C3
C4
C5
C1
C2
C3
C3
C4
C4
C5
C1
Proximity Matrix
C2
C5
...
p1
p2
p3
p4
p9
p10
p11
p12
Intermediate Situation
We want to merge the two closest clusters (C2 and C5) and
update the proximity matrix.
C1 C2
C3
C4 C5
C1
C2
C3
C3
C4
C4
C5
Proximity Matrix
C1
C2
C5
...
p1
p2
p3
p4
p9
p10
p11
p12
After Merging
The question is: “How do we update the proximity matrix?”
C1
C1
C3
C2 U C5
C4
C2
U
C5
C3
C4
?
?
?
?
?
C3
?
C4
?
Proximity Matrix
C1
C2 U C5
...
p1
p2
p3
p4
p9
p10
p11
p12
How to Define Inter-Cluster
Similarity
p1
p2
p3
p4 p5
p1
Similarity?
p2
p3
p4
p5





MIN
MAX
Group Average
Distance Between Centroids
Ward’s Method
.
.
.
Proximity Matrix
...
How to Define Inter-Cluster
Similarity
p1
p2
p3
p4 p5
p1
p2
p3
p4





p5
MIN
MAX
Group Average
Distance Between Centroids
Ward’s Method
.
.
.
Proximity Matrix
...
How to Define Inter-Cluster
Similarity
p1
p2
p3
p4 p5
p1
p2
p3
p4





MIN
MAX
Group Average
Distance Between Centroids
Ward’s Method
p5
.
.
.
Proximity Matrix
...
How to Define Inter-Cluster
Similarity
p1
p2
p3
p4 p5
p1
p2
p3
p4





p5
MIN
MAX
Group Average
Distance Between Centroids
Ward’s Method
.
.
.
Proximity Matrix
...
How to Define Inter-Cluster
Similarity
p1
p2
p3
p4 p5
p1
p2


p3
p4
p5





MIN
MAX
Group Average
Distance Between Centroids
Ward’s Method
.
.
.
Proximity Matrix
...
Ward’s Method
 Similarity of two clusters is based on the increase in
squared error when two clusters are merged
 Less susceptible to noise and outliers
 Biased towards globular clusters
 Hierarchical analogue of K-means
 Can be used to initialize K-means
K-means Clustering





Partitional clustering approach
Each cluster is associated with a centroid (center
point)
Each point is assigned to the cluster with the closest
centroid
Number of clusters, K, must be specified
The basic algorithm is very simple
K-means Clustering – Details
 Initial centroids are often chosen randomly.

Clusters produced vary from one run to another.
 The centroid is (typically) the mean of the
points in the cluster.
 ‘Closeness’ is measured by Euclidean distance,
cosine similarity, correlation, etc.
 K-means will converge for common similarity
measures mentioned above.
 Most of the convergence happens in the first
few iterations.

Often the stopping condition is changed to ‘Until
relatively few points change clusters’
Two different K-means Clustering
Original Points
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2.5
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y
1.5
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-2
-1.5
-1
-0.5
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0.5
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x
2.5
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y
3
1
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0.5
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-1.5
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x
Optimal Clustering
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-1.5
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-0.5
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x
Sub-optimal Clustering
Importance of Choosing Initial
Centroids
Iteration 6
3
Iteration
1
2
Iteration
Iteration 4
35
3
2.5
33
2.52
2.5
2.5
y
2
1.5
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yy
1.51
1.5
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xx
How many clusters
 To decide the appropriate number of clusters
 Run the analysis for different values of k and compare the
solutions with the pseudo-F statistic given by
tr[ B /( K  1)]
pseudo F 
tr[W /(n  K )]
Where B is the inter-cluster sum of squares matrix, W is
the intra-cluster sum of squares, K is the number of
clusters and n the number of data points
The larger the pseudo-F statistic, the more efficient the
clustering is
Evaluating K-means Clusters

Most common measure is Sum of Squared Error (SSE)


For each point, the error is the distance to the nearest
cluster
To get SSE, we square these errors and sum them.
K
SSE    dist2 (mi , x )

i 1 xCi
x is a data point in cluster Ci and mi is the representative
point for cluster Ci



mi corresponds to the center (mean) of the cluster
Given two clusters, we can choose the one with the
smallest error
One easy way to reduce SSE is to increase K, the number
of clusters

A good clustering with smaller K can have a lower SSE than a
poor clustering with higher K
Problems with Selecting Initial Points

If there are K ‘real’ clusters then the chance of
selecting one centroid from each cluster is small.
 Chance is relatively small when K is large
 If clusters are the same size, n, then



For example, if K = 10, then probability =
10!/1010 = 0.00036
Sometimes the initial centroids will readjust
themselves in ‘right’ way, and sometimes they
don’t
Consider an example of five pairs of clusters
Solutions to Initial Centroids
Problem
 Multiple runs
 Helps, but probability is not on your side
 Sample and use hierarchical clustering to
determine initial centroids
 Select more than k initial centroids and
then select among these initial centroids
 Select most widely separated
 Post-processing
 Bisecting K-means
 Not as susceptible to initialization issues
Handling Empty Clusters
 Basic K-means algorithm can yield empty
clusters
 Several strategies
 Choose the point that contributes most to SSE
 Choose a point from the cluster with the highest
SSE
 If there are several empty clusters, the above
can be repeated several times.
Updating Centers Incrementally
 In the basic K-means algorithm, centroids are
updated after all points are assigned to a centroid
 An alternative is to update the centroids after each
assignment (incremental approach)





Each assignment updates zero or two centroids
More expensive
Introduces an order dependency
Never get an empty cluster
Can use “weights” to change the impact
Pre-processing and Postprocessing
 Pre-processing
 Normalize the data
 Eliminate outliers
 Post-processing
 Eliminate small clusters that may represent outliers
 Split ‘loose’ clusters, i.e., clusters with relatively high
SSE
 Merge clusters that are ‘close’ and that have
relatively low SSE
 Can use these steps during the clustering process

ISODATA
Bisecting K-means
 Bisecting K-means algorithm

Variant of K-means that can produce a
partitional or a hierarchical clustering