Clustering
Supervised vs. Unsupervised Learning
Examples of clustering in Web IR
Characteristics of clustering
Clustering algorithms
Cluster Labeling
1
Supervised vs. Unsupervised Learning
Supervised Learning
Goal: A program that performs a task as good as humans.
TASK
–
well defined (the target function)
EXPERIENCE
–
training data provided by a human
PERFORMANCE
–
error/accuracy on the task
Unsupervised Learning
Goal: To find some kind of structure in the data.
TASK
–
vaguely defined
No EXPERIENCE
No PERFORMANCE (but, there are some evaluations metrics)
2
What is Clustering?
Clustering is the most common form of
Unsupervised
Learning
Clustering
is the process of grouping a set of physical or
abstract objects into classes of similar objects
It can be used in IR:
To improve recall in search applications
For better navigation of search results
3
Example 1: Improving Recall
Cluster hypothesis

Documents with similar text are
related
Thus, when a query matches a document
D
, also return
other documents in the cluster containing
D.
4
Example 2: Better Navigation
5
Clustering Characteristics
Flat versus Hierarchical Clustering
Flat means dividing objects in groups (clusters)
Hierarchical means organize clusters in a subsuming hierarchy
Evaluating Clustering
Internal Criteria
The intra

cluster similarity is high (tightness)
The inter

cluster similarity is low (separateness)
External Criteria
Did we discover the hidden classes? (we need gold
standard data for this evaluation)
6
Clustering for Web IR
Representation for clustering
Document representation
Vector space? Normalization?
Need a notion of similarity/distance
How many clusters?
Fixed a priori?
Completely data driven?
Avoid “trivial” clusters

too large or small
7
Recall documents as vectors
Each doc
j
is a vector of
tf
idf
values, one component for
each term.
Can normalize to unit length.
So we have a vector space
terms are axes

aka
features
n
docs live in this space
even with stemming, may have 20,000+ dimensions
8
i
j
i
j
i
n
i
j
i
j
i
j
j
j
idf
tf
w
w
w
d
d
d
,
,
1
,
,
where
What makes documents related?
Ideal: semantic similarity.
Practical: statistical similarity
We will use cosine similarity.
Documents as vectors.
We will describe algorithms in terms of cosine similarity.
9
n
i
k
i
w
j
i
w
j
d
sim
d
d
k
d
k
j
1
,
,
)
(
:
,
normalized
of
similarity
Cosine
,
This is known as the normalized inner product.
Intuition for relatedness
10
t 1
D2
D1
D3
D4
t 2
x
y
Documents that are “close together”
in vector space talk about the same things.
Clustering Algorithms
Partitioning “flat” algorithms
Usually start with a random (partial) partitioning
Refine it iteratively
k

means clustering
Model based clustering
(we will not cover it)
Hierarchical algorithms
Bottom

up, agglomerative
Top

down, divisive
(we will not cover it)
11
Partitioning “flat” algorithms
Partitioning method: Construct a partition of
n
documents
into a set of
k
clusters
Given: a set of documents and the number
k
Find: a partition of
k
clusters that optimizes the chosen
partitioning criterion
12
Watch animation of k

means
K

means
Assumes documents are real

valued vectors.
Clusters based on
centroids
(aka the
center of gravity
or
mean) of points in a cluster,
c
:
Reassignment of instances to clusters is based on distance
to the current cluster centroids.
13
c
x
x
c


1
(c)
μ
K

Means Algorithm
14
Let
d
be the distance measure between instances.
Select
k
random instances {
s
1
,
s
2
,…
s
k
} as seeds.
Until clustering converges or other stopping criterion:
For each instance
x
i
:
Assign
x
i
to the cluster
c
j
such that
d
(
x
i
,
s
j
) is minimal.
(
Update the seeds to the
centroid
of each cluster
)
For each cluster
c
j
s
j
=
(
c
j
)
K

means: Different Issues
When to stop?
When a fixed number of iterations is reached
When centroid positions do not change
Seed Choice
Results can vary based on random seed selection.
Try out multiple starting points
15
Example showing
sensitivity to seeds
A
B
D
E
C
F
If you start with B and E
as centroids
you converge to {A,B,C}
and {D,E,F}
If you start with D and F
you converge to
{A,B,D,E} {C,F}
Hierarchical clustering
Build a tree

based hierarchical taxonomy (
dendrogram
)
from a set of unlabeled examples.
16
animal
vertebrate
fish reptile amphib. mammal worm insect crustacean
invertebrate
Hierarchical Agglomerative Clustering
We assume there is a similarity function that determines
the similarity of two instances.
17
Start with all instances in their own cluster.
Until there is only one cluster:
Among the current clusters, determine the two
clusters,
c
i
and
c
j
, that are most similar.
Replace
c
i
and
c
j
with a single cluster
c
i
c
j
Algorithm:
Watch animation of HAC
What is the most similar cluster?
Single

link
Similarity of the most cosine

similar (single

link)
Complete

link
Similarity of the “furthest” points, the least cosine

similar
Group

average agglomerative clustering
Average cosine between pairs of elements
Centroid clustering
Similarity of clusters’ centroids
18
Single link clustering
19
1) Use maximum similarity of pairs:
)
,
(
max
)
,
(
,
y
x
sim
c
c
sim
j
i
c
y
c
x
j
i
2) After merging
c
i
and
c
j
, the similarity of the resulting cluster to
another cluster,
c
k
, is:
))
,
(
),
,
(
max(
)
),
((
k
j
k
i
k
j
i
c
c
sim
c
c
sim
c
c
c
sim
Complete link clustering
20
1) Use minimum similarity of pairs:
)
,
(
min
)
,
(
,
y
x
sim
c
c
sim
j
i
c
y
c
x
j
i
2) After merging
c
i
and
c
j
, the similarity of the resulting cluster to
another cluster,
c
k
, is:
))
,
(
),
,
(
min(
)
),
((
k
j
k
i
k
j
i
c
c
sim
c
c
sim
c
c
c
sim
Major issue

labeling
After clustering algorithm finds clusters

how can they be
useful to the end user?
Need a concise label for each cluster
In search results, say “Animal” or “Car” in the
jaguar
example.
In topic trees (Yahoo), need navigational cues.
Often done by hand, a posteriori.
21
How to Label Clusters
Show titles of typical documents
Titles are easy to scan
Authors create them for quick scanning!
But you can only show a few titles which may not fully represent
cluster
Show words/phrases prominent in cluster
More likely to fully represent cluster
Use distinguishing words/phrases
But harder to scan
22
Not covered in this lecture
Complexity:
Clustering is computationally expensive. Implementations need
careful balancing of needs.
How to decide how many clusters are best?
Evaluating the “goodness” of clustering
There are many techniques, some focus on implementation issues
(complexity/time), some on the quality of
23
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