lecture12-clustering.ppt

Introduction to Information Retrieval
Introduction to
Information Retrieval
CS276: Information Retrieval and Web Search
Pandu Nayak and Prabhakar Raghavan
Lecture 12: Clustering

Today’s Topic: Clustering
 Document clustering
 Motivations
 Document representations
 Success criteria
 Clustering algorithms
 Partitional
 Hierarchical

What is clustering?
 Clustering: the process of grouping a set of objects
into classes of similar objects
 Documents within a cluster should be similar.
 Documents from different clusters should be
dissimilar.
 The commonest form of unsupervised learning
 Unsupervised learning = learning from raw data, as
opposed to supervised data where a classification of
examples is given
 A common and important task that finds many
applications in IR and other places
Ch. 16

A data set with clear cluster structure
 How would
you design
an algorithm
for finding
the three
clusters in
this case?
Ch. 16

Applications of clustering in IR
 Whole corpus analysis/navigation
 Better user interface: search without typing
 For improving recall in search applications
 Better search results (like pseudo RF)
 For better navigation of search results
 Effective “user recall” will be higher
 For speeding up vector space retrieval
 Cluster-based retrieval gives faster search
Sec. 16.1

Yahoo! Hierarchy isn’t clustering but is the kind
of output you want from clustering
dairy
crops
agronomy
forestry
AI
HCI
craft
missions
botany
evolution
cell
magnetism
relativity
courses
agriculture biology physics CS space
... ... ...
… (30)
www.yahoo.com/Science
... ...

Google News: automatic clustering gives an
effective news presentation metaphor

Scatter/Gather: Cutting, Karger, and Pedersen
Sec. 16.1

For visualizing a document collection and its
themes
 Wise et al, “Visualizing the non-visual” PNNL
 ThemeScapes, Cartia
 [Mountain height = cluster size]

For improving search recall
 Cluster hypothesis - Documents in the same cluster behave similarly
with respect to relevance to information needs
 Therefore, to improve search recall:
 Cluster docs in corpus a priori
 When a query matches a doc D, also return other docs in the
cluster containing D
 Hope if we do this: The query “car” will also return docs containing
automobile
 Because clustering grouped together docs containing car with
those containing automobile.
Why might this happen?
Sec. 16.1

11
yippy.com – grouping search results

Issues for clustering
 Representation for clustering
 Document representation
 Vector space? Normalization?
 Centroids aren’t length normalized
 Need a notion of similarity/distance
 How many clusters?
 Fixed a priori?
 Completely data driven?
 Avoid “trivial” clusters - too large or small
 If a cluster's too large, then for navigation purposes you've
wasted an extra user click without whittling down the set of
documents much.
Sec. 16.2

Notion of similarity/distance
 Ideal: semantic similarity.
 Practical: term-statistical similarity
 We will use cosine similarity.
 Docs as vectors.
 For many algorithms, easier to think in
terms of a distance (rather than similarity)
between docs.
 We will mostly speak of Euclidean distance
 But real implementations use cosine similarity

Clustering Algorithms
 Flat algorithms
 Usually start with a random (partial) partitioning
 Refine it iteratively
 K means clustering
 (Model based clustering)
 Hierarchical algorithms
 Bottom-up, agglomerative
 (Top-down, divisive)

Hard vs. soft clustering
 Hard clustering: Each document belongs to exactly one cluster
 More common and easier to do
 Soft clustering: A document can belong to more than one
cluster.
 Makes more sense for applications like creating browsable
hierarchies
 You may want to put a pair of sneakers in two clusters: (i) sports
apparel and (ii) shoes
 You can only do that with a soft clustering approach.
 We won’t do soft clustering today. See IIR 16.5, 18

Partitioning 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
 Globally optimal
 Intractable for many objective functions
 Ergo, exhaustively enumerate all partitions
 Effective heuristic methods: K-means and K-
medoids algorithms
See also Kleinberg NIPS 2002 – impossibility for natural clustering

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.
 (Or one can equivalently phrase it in terms of similarities)



c
x
x
c 


|
|
1
(c)
μ
Sec. 16.4

K-Means Algorithm
Select K random docs {s1, s2,… sK} as seeds.
Until clustering converges (or other stopping criterion):
For each doc di:
Assign di to the cluster cj such that dist(xi, sj) is minimal.
(Next, update the seeds to the centroid of each cluster)
For each cluster cj
sj = (cj)
Sec. 16.4

K Means Example
(K=2)
Pick seeds
Reassign clusters
Compute centroids
x
x
Reassign clusters
x
x x
x Compute centroids
Reassign clusters
Converged!
Sec. 16.4

Termination conditions
 Several possibilities, e.g.,
 A fixed number of iterations.
 Doc partition unchanged.
 Centroid positions don’t change.
Does this mean that the docs in a
cluster are unchanged?
Sec. 16.4

Convergence
 Why should the K-means algorithm ever reach a
fixed point?
 A state in which clusters don’t change.
 K-means is a special case of a general procedure
known as the Expectation Maximization (EM)
algorithm.
 EM is known to converge.
 Number of iterations could be large.
 But in practice usually isn’t
Sec. 16.4

Convergence of K-Means
 Define goodness measure of cluster k as sum of
squared distances from cluster centroid:
 Gk = Σi (di – ck)2 (sum over all di in cluster k)
 G = Σk Gk
 Reassignment monotonically decreases G since
each vector is assigned to the closest centroid.
Lower case!
Sec. 16.4

Convergence of K-Means
 Recomputation monotonically decreases each Gk
since (mk is number of members in cluster k):
 Σ (di – a)2 reaches minimum for:
 Σ –2(di – a) = 0
 Σ di = Σ a
 mK a = Σ di
 a = (1/ mk) Σ di = ck
 K-means typically converges quickly
Sec. 16.4

Time Complexity
 Computing distance between two docs is O(M)
where M is the dimensionality of the vectors.
 Reassigning clusters: O(KN) distance computations,
or O(KNM).
 Computing centroids: Each doc gets added once to
some centroid: O(NM).
 Assume these two steps are each done once for I
iterations: O(IKNM).
Sec. 16.4

Seed Choice
 Results can vary based on
random seed selection.
 Some seeds can result in poor
convergence rate, or
convergence to sub-optimal
clusterings.
 Select good seeds using a heuristic
(e.g., doc least similar to any
existing mean)
 Try out multiple starting points
 Initialize with the results of another
method.
In the above, 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}
Example showing
sensitivity to seeds
Sec. 16.4

K-means issues, variations, etc.
 Recomputing the centroid after every assignment
(rather than after all points are re-assigned) can
improve speed of convergence of K-means
 Assumes clusters are spherical in vector space
 Sensitive to coordinate changes, weighting etc.
 Disjoint and exhaustive
 Doesn’t have a notion of “outliers” by default
 But can add outlier filtering
Sec. 16.4
Dhillon et al. ICDM 2002 – variation to fix some issues with small
document clusters

How Many Clusters?
 Number of clusters K is given
 Partition n docs into predetermined number of clusters
 Finding the “right” number of clusters is part of the
problem
 Given docs, partition into an “appropriate” number of
subsets.
 E.g., for query results - ideal value of K not known up front
- though UI may impose limits.
 Can usually take an algorithm for one flavor and
convert to the other.

K not specified in advance
 Say, the results of a query.
 Solve an optimization problem: penalize having
lots of clusters
 application dependent, e.g., compressed summary
of search results list.
 Tradeoff between having more clusters (better
focus within each cluster) and having too many
clusters

K not specified in advance
 Given a clustering, define the Benefit for a
doc to be the cosine similarity to its
centroid
 Define the Total Benefit to be the sum of
the individual doc Benefits.
Why is there always a clustering of Total Benefit n?

Penalize lots of clusters
 For each cluster, we have a Cost C.
 Thus for a clustering with K clusters, the Total Cost is
KC.
 Define the Value of a clustering to be =
Total Benefit - Total Cost.
 Find the clustering of highest value, over all choices
of K.
 Total benefit increases with increasing K. But can stop
when it doesn’t increase by “much”. The Cost term
enforces this.

Hierarchical Clustering
 Build a tree-based hierarchical taxonomy
(dendrogram) from a set of documents.
 One approach: recursive application of a
partitional clustering algorithm.
animal
vertebrate
fish reptile amphib. mammal worm insect crustacean
invertebrate
Ch. 17

Dendrogram: Hierarchical Clustering
 Clustering obtained
by cutting the
dendrogram at a
desired level: each
connected
component forms a
cluster.
32

Hierarchical Agglomerative Clustering
(HAC)
 Starts with each doc in a separate cluster
 then repeatedly joins the closest pair of
clusters, until there is only one cluster.
 The history of merging forms a binary tree
or hierarchy.
Sec. 17.1
Note: the resulting clusters are still “hard” and induce a partition

Closest pair of clusters
 Many variants to defining closest pair of clusters
 Single-link
 Similarity of the most cosine-similar (single-link)
 Complete-link
 Similarity of the “furthest” points, the least cosine-similar
 Centroid
 Clusters whose centroids (centers of gravity) are the most
cosine-similar
 Average-link
 Average cosine between pairs of elements
Sec. 17.2

Single Link Agglomerative Clustering
 Use maximum similarity of pairs:
 Can result in “straggly” (long and thin) clusters
due to chaining effect.
 After merging ci and cj, the similarity of the
resulting cluster to another cluster, ck, is:
)
,
(
max
)
,
(
,
y
x
sim
c
c
sim
j
i c
y
c
x
j
i



))
,
(
),
,
(
max(
)
),
(( k
j
k
i
k
j
i c
c
sim
c
c
sim
c
c
c
sim 

Sec. 17.2

Single Link Example
Sec. 17.2

Complete Link
 Use minimum similarity of pairs:
 Makes “tighter,” spherical clusters that are typically
preferable.
 After merging ci and cj, the similarity of the resulting
cluster to another cluster, ck, is:
)
,
(
min
)
,
(
,
y
x
sim
c
c
sim
j
i c
y
c
x
j
i



))
,
(
),
,
(
min(
)
),
(( k
j
k
i
k
j
i c
c
sim
c
c
sim
c
c
c
sim 

Ci Cj Ck
Sec. 17.2

Complete Link Example
Sec. 17.2

Computational Complexity
 In the first iteration, all HAC methods need to
compute similarity of all pairs of N initial instances,
which is O(N2).
 In each of the subsequent N2 merging iterations,
compute the distance between the most recently
created cluster and all other existing clusters.
 In order to maintain an overall O(N2) performance,
computing similarity to each other cluster must be
done in constant time.
 Often O(N3) if done naively or O(N2 log N) if done more
cleverly
Sec. 17.2.1

Group Average
 Similarity of two clusters = average similarity of all pairs
within merged cluster.
 Compromise between single and complete link.
 Two options:
 Averaged across all ordered pairs in the merged cluster
 Averaged over all pairs between the two original clusters
 No clear difference in efficacy
 

 






)
( :
)
(
)
,
(
)
1
(
1
)
,
(
j
i j
i
c
c
x x
y
c
c
y
j
i
j
i
j
i y
x
sim
c
c
c
c
c
c
sim
 




Sec. 17.3

Computing Group Average Similarity
 Always maintain sum of vectors in each cluster.
 Compute similarity of clusters in constant time:



j
c
x
j x
c
s



)
(
)
1
|
|
|
|)(|
|
|
(|
|)
|
|
(|
))
(
)
(
(
))
(
)
(
(
)
,
(









j
i
j
i
j
i
j
i
j
i
j
i
c
c
c
c
c
c
c
s
c
s
c
s
c
s
c
c
sim




Sec. 17.3

What Is A Good Clustering?
 Internal criterion: A good clustering will produce
high quality clusters in which:
 the intra-class (that is, intra-cluster) similarity is
high
 the inter-class similarity is low
 The measured quality of a clustering depends on
both the document representation and the
similarity measure used
Sec. 16.3

External criteria for clustering quality
 Quality measured by its ability to discover some
or all of the hidden patterns or latent classes in
gold standard data
 Assesses a clustering with respect to ground truth
… requires labeled data
 Assume documents with C gold standard classes,
while our clustering algorithms produce K clusters,
ω1, ω2, …, ωK with ni members.
Sec. 16.3

External Evaluation of Cluster Quality
 Simple measure: purity, the ratio between the
dominant class in the cluster πi and the size of
cluster ωi
 Biased because having n clusters maximizes
purity
 Others are entropy of classes in clusters (or
mutual information between classes and
clusters)
C
j
n
n
Purity ij
j
i
i 
 )
(
max
1
)
(
Sec. 16.3

 
 
 
 
 
 
 
 

Cluster I Cluster II Cluster III
Cluster I: Purity = 1/6 (max(5, 1, 0)) = 5/6
Cluster II: Purity = 1/6 (max(1, 4, 1)) = 4/6
Cluster III: Purity = 1/5 (max(2, 0, 3)) = 3/5
Purity example
Sec. 16.3

Rand Index measures between pair
decisions. Here RI = 0.68
Number of
points
Same Cluster
in clustering
Different
Clusters in
clustering
Same class in
ground truth 20 24
Different
classes in
ground truth
20 72
Sec. 16.3

Rand index and Cluster F-measure
B
A
A
P


D
C
B
A
D
A
RI





C
A
A
R


Compare with standard Precision and Recall:
People also define and use a cluster F-measure,
which is probably a better measure.
Sec. 16.3

Final word and resources
 In clustering, clusters are inferred from the data without
human input (unsupervised learning)
 However, in practice, it’s a bit less clear: there are many
ways of influencing the outcome of clustering: number of
clusters, similarity measure, representation of documents, .
. .
 Resources
 IIR 16 except 16.5
 IIR 17.1–17.3

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