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807 - TEXT ANALYTICS. Massimo Poesio Lecture 3: (Document) clustering. Clustering. Or, discovering similarities between objects Individuals, Documents, … Applications: Recommender systems Document organization. Recommending: restaurants. We have a list of all Wivenhoe restaurants

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807 text analytics

807 - TEXT ANALYTICS

Massimo PoesioLecture 3: (Document) clustering

clustering
Clustering
  • Or, discovering similarities between objects
    • Individuals, Documents, …
  • Applications:
    • Recommender systems
    • Document organization
recommending restaurants
Recommending: restaurants
  • We have a list of allWivenhoe restaurants
    • with  and  ratings for some
    • as provided by someUni Essex students / staff
  • Which restaurant(s) should I recommend to you?
algorithm 0
Algorithm 0
  • Recommend to you the most popular restaurants
    • say # positive votes minus # negative votes
  • Ignores your culinary preferences
    • And judgements of those with similar preferences
  • How can we exploit the wisdom of “like-minded” people?
now that we have a matrix
Now that we have a matrix

View all other entries as zeros for now.

preference defined data space

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PREFERENCE-DEFINED DATA SPACE

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similarity between two people
Similarity between two people
  • Similarity between their preference vectors.
  • Inner products are a good start.
  • Dave has similarity 3 with Estie
    • but -2 with Cindy.
  • Perhaps recommend Black Buoy to Dave
    • andBakehouse to Bob, etc.
algorithm 1 1
Algorithm 1.1
  • You give me your preferences and I need to give you a recommendation.
  • I find the person “most similar” to you in my database and recommend something he likes.
  • Aspects to consider:
    • No attempt to discern cuisines, etc.
    • What if you’ve been to all the restaurants he has?
    • Do you want to rely on one person’s opinions?
algorithm 1 k
Algorithm 1.k
  • You give me your preferences and I need to give you a recommendation.
  • I find the k people “most similar” to you in my database and recommend what’s most popular amongst them.
  • Issues:
    • A priori unclear what k should be
    • Risks being influenced by “unlike minds”
slightly more sophisticated attempt
Slightly more sophisticated attempt
  • Group similar users together into clusters
  • You give your preferences and seek a recommendation, then
    • Find the “nearest cluster” (what’s this?)
    • Recommend the restaurants most popular in this cluster
  • Features:
    • avoids data sparsity issues
    • still no attempt to discern why you’re recommended what you’re recommended
    • how do you cluster?
clusters

Cindy

Alice, Bob

Fred ..

Dave, Estie

….

CLUSTERS
  • Can cluster
how do you cluster
How do you cluster?
  • Must keep similar people together in a cluster
  • Separate dissimilar people
  • Factors:
    • Need a notion of similarity/distance
    • Vector space? Normalization?
    • How many clusters?
      • Fixed a priori?
      • Completely data driven?
    • Avoid “trivial” clusters - too large or small
looking beyond

Clustering other things

(documents, web pages)

Other approaches

to recommendation

General unsupervised machine learning.

Looking beyond

Clustering people for

restaurant recommendations

Amazon.com

why cluster documents
Why cluster documents?
  • For improving recall in search applications
  • For speeding up vector space retrieval
  • Corpus analysis/navigation
    • Sense disambiguation in search results
improving search recall
Improving search recall
  • Cluster hypothesis - Documents with similar text are related
  • Ergo, 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: docs containing automobile returned on a query for car because
    • clustering grouped together docs containing car with those containing automobile.

Why might this happen?

speeding up vector space retrieval
Speeding up vector space retrieval
  • In vector space retrieval, must find nearest doc vectors to query vector
  • This would entail finding the similarity of the query to every doc - slow!
  • By clustering docs in corpus a priori
    • find nearest docs in cluster(s) close to query
    • inexact but avoids exhaustive similarity computation

Exercise: Make up a simple example with points on a line in 2 clusters where this inexactness shows up.

corpus analysis navigation
Corpus analysis/navigation
  • Given a corpus, partition it into groups of related docs
    • Recursively, can induce a tree of topics
    • Allows user to browse through corpus to home in on information
    • Crucial need: meaningful labels for topic nodes.
navigating search results
Navigating search results
  • Given the results of a search (say jaguar), partition into groups of related docs
    • sense disambiguation
  • Approach followed by Uni Essex site
    • Kruschwitz / al Bakouri / Lungley
  • Other examples: IBM InfoSphere Data Explorer
    • (was: vivisimo.com)
results list clustering example
Results list clustering example
  • Cluster 1:
    • Jaguar Motor Cars’ home page
    • Mike’s XJS resource page
    • Vermont Jaguar owners’ club
  • Cluster 2:
    • Big cats
    • My summer safari trip
    • Pictures of jaguars, leopards and lions
  • Cluster 3:
    • Jacksonville Jaguars’ Home Page
    • AFC East Football Teams
what makes docs related
What makes docs “related”?
  • Ideal: semantic similarity.
  • Practical: 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 describe algorithms in terms of cosine similarity.
documents as bags of words
DOCUMENTS AS BAGS OF WORDS

INDEX

DOCUMENT

broadmay rallyrallied

signal stockstocks techtechnology traderstraders trend

broad tech stock rally may signal trend - traders.

technology stocks rallied on tuesday, with gains scored broadly across many sectors, amid what some traders called a recovery from recent doldrums.

d oc as vector
Doc as vector
  • 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 10000+ dimensions
    • do we really want to use all terms?
term weighting in vector space models the tf idf measure
TERM WEIGHTING IN VECTOR SPACE MODELS: THE TF.IDF MEASURE

FREQUENCY of term i in document k

Number of documents with term i

intuition
Intuition

t 3

D2

D3

D1

x

y

t 1

t 2

D4

Postulate: Documents that are “close together”

in vector space talk about the same things.

two flavors of clustering
Two flavors of clustering
  • Given n docs and a positive integer k, partition docs into k (disjoint) subsets.
  • 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.
thought experiment
Thought experiment
  • Consider clustering a large set of computer science documents
    • what do you expect to see in the vector space?
thought experiment1

Arch.

Graphics

Theory

NLP

AI

Thought experiment
  • Consider clustering a large set of computer science documents
    • what do you expect to see in the vector space?
decision boundaries

Arch.

Graphics

Theory

NLP

AI

Decision boundaries
  • Could we use these blobs to infer the subject of a new document?
deciding what a new doc is about

Arch.

Graphics

Theory

NLP

AI

= AI

Deciding what a new doc is about
  • Check which region the new doc falls into
    • can output “softer” decisions as well.
setup
Setup
  • Given “training” docs for each category
    • Theory, AI, NLP, etc.
  • Cast them into a decision space
    • generally a vector space with each doc viewed as a bag of words
  • Build a classifier that will classify new docs
    • Essentially, partition the decision space
  • Given a new doc, figure out which partition it falls into
supervised vs unsupervised learning
Supervised vs. unsupervised learning
  • This setup is called supervised learning in the terminology of Machine Learning
  • In the domain of text, various names
    • Text classification, text categorization
    • Document classification/categorization
    • “Automatic” categorization
    • Routing, filtering …
  • In contrast, the earlier setting of clustering is called unsupervised learning
    • Presumes no availability of training samples
    • Clusters output may not be thematically unified.
which is better
“Which is better?”
  • Depends
    • on your setting
    • on your application
  • Can use in combination
    • Analyze a corpus using clustering
    • Hand-tweak the clusters and label them
    • Use clusters as training input for classification
    • Subsequent docs get classified
  • Computationally, methods quite different
what more can these methods do
What more can these methods do?
  • Assigning a category label to a document is one way of adding structure to it.
  • Can add others, e.g.,extract from the doc
    • people
    • places
    • dates
    • organizations …
  • This process is known as information extraction
    • can also be addressed using supervised learning.
clustering a bit of terminology
Clustering: a bit of terminology
  • REPRESENTATIVE
  • CENTROID
  • OUTLIER
key notion cluster representative
Key notion: cluster representative
  • In the algorithms to follow, will generally need a notion of a representative point in a cluster
  • Representative should be some sort of “typical” or central point in the cluster, e.g.,
    • point inducing smallest radii to docs in cluster
    • smallest squared distances, etc.
    • point that is the “average” of all docs in the cluster
  • Need not be a document
key notion cluster centroid

Centroid

Key notion: cluster centroid
  • Centroid of a cluster = component-wise average of vectors in a cluster - is a vector.
    • Need not be a doc.
  • Centroid of (1,2,3); (4,5,6); (7,2,6) is (4,3,5).
outliers in centroid computation

Centroid

(Outliers in centroid computation)
  • Can ignore outliers when computing centroid.
  • What is an outlier?
    • Lots of statistical definitions, e.g.
    • moment of point to centroid > M  some cluster moment.

Say 10.

Outlier

clustering algorithms
Clustering algorithms
  • Partitional vs. hierarchical
  • Agglomerative
  • K-means
hierarchical clustering
Hierarchical Clustering

Traditional Hierarchical Clustering

Traditional Dendrogram

Non-traditional Hierarchical Clustering

Non-traditional Dendrogram

agglomerative clustering
Agglomerative clustering
  • Given target number of clusters k.
  • Initially, each doc viewed as a cluster
    • start with n clusters;
  • Repeat:
    • while there are > k clusters, find the “closest pair” of clusters and merge them.
closest pair of clusters
“Closest pair” of clusters
  • Many variants to defining closest pair of clusters
    • Clusters whose centroids are the most cosine-similar
    • … whose “closest” points are the most cosine-similar
    • … whose “furthest” points are the most cosine-similar
example n 6 k 3 closest pair of centroids

Centroid after

second step.

Centroid after first step.

Example: n=6, k=3, closest pair of centroids

d4

d6

d3

d5

d1

d2

issues
Issues
  • Have to support finding closest pairs continually
    • compare all pairs?
      • Potentially n3 cosine similarity computations
      • To avoid: use approximations.
    • “points” are changing as centroids change.
  • Changes at each step are not localized
    • on a large corpus, memory management an issue
    • sometimes addressed by clustering a sample.

Why?

What’s

this?

exercise
Exercise
  • Consider agglomerative clustering on n points on a line. Explain how you could avoid n3 distance computations - how many will your scheme use?
using approximations
“Using approximations”
  • In standard algorithm, must find closest pair of centroids at each step
  • Approximation: instead, find nearly closest pair
    • use some data structure that makes this approximation easier to maintain
    • simplistic example: maintain closest pair based on distances in projection on a random line

Random line

a partitional clustering algorithm k means
A partitional clustering algorithm:K-MEANS
  • Given k - the number of clusters desired.
  • Each cluster associated with a centroid.
  • Each point assigned to the cluster with the closest centroid.
  • Iterate.
basic iteration
Basic iteration
  • At the start of the iteration, we have k centroids.
  • Each doc assigned to the nearest centroid.
  • All docs assigned to the same centroid are averaged to compute a new centroid;
    • thus have k new centroids.
iteration example
Iteration example

Docs

Current centroids

iteration example1
Iteration example

Docs

New centroids

k means clustering details
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’
  • Complexity is O( n * K * I * d )
    • n = number of points, K = number of clusters, I = number of iterations, d = number of attributes
termination conditions
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?

convergence
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 EM algorithm.
    • Under reasonable conditions, known to converge.
    • Number of iterations could be large.
exercise1
Exercise
  • Consider running 2-means clustering on a corpus, each doc of which is from one of two different languages. What are the two clusters we would expect to see?
  • Is agglomerative clustering likely to produce different results?
k not specified in advance
k not specified in advance
  • Say, the results of a query.
  • Solve an optimization problem: penalize having lots of clusters
    • application dependant, 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 advance1
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
Penalize lots of clusters
  • For each cluster, we have a CostC.
  • Thus for a clustering with k clusters, the Total Cost is kC.
  • Define the Value of a cluster to be =

Total Benefit - Total Cost.

  • Find the clustering of highest Value, over all choices of k.
evaluating k means clusters
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.
    • x is a data point in cluster Ci and mi is the representative point for cluster Ci
      • can show that micorresponds 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
limitations of k means
Limitations of K-means
  • K-means has problems when clusters are of differing
    • Sizes
    • Densities
    • Non-globular shapes
  • K-means has problems when the data contains outliers.
limitations of k means differing sizes
Limitations of K-means: Differing Sizes

K-means (3 Clusters)

Original Points

limitations of k means differing density
Limitations of K-means: Differing Density

K-means (3 Clusters)

Original Points

limitations of k means non globular shapes
Limitations of K-means: Non-globular Shapes

Original Points

K-means (2 Clusters)

hierarchical clustering1

d3,d4,d5

d4,d5

d3

Hierarchical clustering
  • As clusters agglomerate, docs likely to fall into a hierarchy of “topics” or concepts.

d3

d5

d1

d4

d2

d1,d2

hierarchical clustering2
Hierarchical Clustering
  • Produces a set of nested clusters organized as a hierarchical tree
  • Can be visualized as a dendrogram
    • A tree like diagram that records the sequences of merges or splits
strengths of hierarchical clustering
Strengths of Hierarchical Clustering
  • Do not have to assume any particular number of clusters
    • Any desired number of clusters can be obtained by ‘cutting’ the dendogram at the proper level
  • They may correspond to meaningful taxonomies
    • Example in biological sciences (e.g., animal kingdom, phylogeny reconstruction, …)
hierarchical clustering3
Hierarchical Clustering
  • Two main types of hierarchical clustering
    • Agglomerative:
      • Start with the points as individual clusters
      • At each step, merge the closest pair of clusters until only one cluster (or k clusters) left
    • Divisive:
      • Start with one, all-inclusive cluster
      • At each step, split a cluster until each cluster contains a point (or there are k clusters)
  • Traditional hierarchical algorithms use a similarity or distance matrix
    • Merge or split one cluster at a time
hierarchical agglomerative clustering hac
Hierarchical Agglomerative Clustering (HAC)
  • More popular hierarchical clustering technique
  • Assumes a similarity function for determining the similarity of two instances.
  • Starts with all instances in a separate cluster and then repeatedly joins the two clusters that are most similar until there is only one cluster.
  • The history of merging forms a binary tree or hierarchy.
agglomerative clustering algorithm
Agglomerative Clustering Algorithm
  • Basic algorithm is straightforward
    • 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
  • Key operation is the computation of the proximity of two clusters
    • Different approaches to defining the distance between clusters distinguish the different algorithms
starting situation

p1

p2

p3

p4

p5

. . .

p1

p2

p3

p4

p5

.

.

.

Starting Situation
  • Start with clusters of individual points and a proximity matrix

Proximity Matrix

intermediate situation

C1

C2

C3

C4

C5

C1

C2

C3

C4

C5

Intermediate Situation
  • After some merging steps, we have some clusters

C3

C4

Proximity Matrix

C1

C5

C2

intermediate situation1

C1

C2

C3

C4

C5

C1

C2

C3

C4

C5

Intermediate Situation
  • We want to merge the two closest clusters (C2 and C5) and update the proximity matrix.

C3

C4

Proximity Matrix

C1

C5

C2

after merging
After Merging

C2 U C5

  • The question is “How do we update the proximity matrix?”

C1

C3

C4

C1

?

? ? ? ?

C2 U C5

C3

C3

?

C4

?

C4

Proximity Matrix

C1

C2 U C5

how to define inter cluster similarity

p1

p2

p3

p4

p5

. . .

p1

p2

p3

p4

p5

.

.

.

How to Define Inter-Cluster Similarity

Similarity?

  • MIN
  • MAX
  • Group Average
  • Distance Between Centroids
  • Other methods driven by an objective function
    • Ward’s Method uses squared error

Proximity Matrix

how to define inter cluster similarity1

p1

p2

p3

p4

p5

. . .

p1

p2

p3

p4

p5

.

.

.

How to Define Inter-Cluster Similarity
  • MIN
  • MAX
  • Group Average
  • Distance Between Centroids
  • Other methods driven by an objective function
    • Ward’s Method uses squared error

Proximity Matrix

how to define inter cluster similarity2

p1

p2

p3

p4

p5

. . .

p1

p2

p3

p4

p5

.

.

.

How to Define Inter-Cluster Similarity
  • MIN
  • MAX
  • Group Average
  • Distance Between Centroids
  • Other methods driven by an objective function
    • Ward’s Method uses squared error

Proximity Matrix

how to define inter cluster similarity3

p1

p2

p3

p4

p5

. . .

p1

p2

p3

p4

p5

.

.

.

How to Define Inter-Cluster Similarity
  • MIN
  • MAX
  • Group Average
  • Distance Between Centroids
  • Other methods driven by an objective function
    • Ward’s Method uses squared error

Proximity Matrix

list of issues applications covered
List of issues/applications covered
  • Term vs. document space clustering
  • Multi-lingual docs
  • Feature selection
  • Speeding up scoring
  • Building navigation structures
    • “Automatic taxonomy induction”
  • Labeling
term vs document space
Term vs. document space
  • Thus far, we clustered docs based on their similarities in terms space
  • For some applications, e.g., topic analysis for inducing navigation structures, can “dualize”:
    • use docs as axes
    • represent (some) terms as vectors
    • proximity based on co-occurrence of terms in docs
    • now clustering terms, not docs
term vs document space1
Term vs. document space
  • If terms carefully chosen (say nouns)
    • fixed number of pairs for distance computation
      • independent of corpus size
    • clusters have clean descriptions in terms of noun phrase co-occurrence - easier labeling?
    • left with problem of binding docs to these clusters
multi lingual docs
Multi-lingual docs
  • E.g., Canadian government docs.
  • Every doc in English and equivalent French.
    • Must cluster by concepts rather than language
  • Simplest: pad docs in one lang with dictionary equivalents in the other
    • thus each doc has a representation in both languages
  • Axes are terms in both languages
feature selection
Feature selection
  • Which terms to use as axes for vector space?
  • Huge body of (ongoing) research
  • IDF is a form of feature selection
    • can exaggerate noise e.g., mis-spellings
  • Pseudo-linguistic heuristics, e.g.,
    • drop stop-words
    • stemming/lemmatization
    • use only nouns/noun phrases
  • Good clustering should “figure out” some of these
clustering to speed up scoring
Clustering to speed up scoring
  • From CS276a, recall sampling and pre-grouping
    • Wanted to find, given a query Q, the nearest docs in the corpus
    • Wanted to avoid computing cosine similarity of Q to each of n docs in the corpus.
sampling and pre grouping
Sampling and pre-grouping
  • First run a clustering phase
    • pick a representative leader for each cluster.
  • Process a query as follows:
    • Given query Q, find its nearest leader L.
    • Seek nearest docs from L’s followers only
      • avoid cosine similarity to all docs.
navigation structure
Navigation structure
  • Given a corpus, agglomerate into a hierarchy
  • Throw away lower layers so you don’t have n leaf topics each having a single doc
    • Many principled methods for this pruning such as MDL.

d3

d5

d1

d3,d4,d5

d4

d2

d1,d2

d4,d5

d3

navigation structure1
Navigation structure
  • Can also induce hierarchy top-down - e.g., use k-means, then recur on the clusters.
    • Need to figure out what k should be at each point.
  • Topics induced by clustering need human ratification
    • can override mechanical pruning.
  • Need to address issues like partitioning at the top level by language.
major issue labeling
Major issue - labeling
  • After clustering algorithm finds clusters - how can they be useful to the end user?
  • Need pithy label for each cluster
    • In search results, say “Football” or “Car” in the jaguar example.
    • In topic trees, need navigational cues.
      • Often done by hand, a posteriori.
how to label clusters
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
labeling
Labeling
  • Common heuristics - list 5-10 most frequent terms in the centroid vector.
    • Drop stop-words; stem.
  • Differential labeling by frequent terms
    • Within the cluster “Computers”, child clusters all have the word computer as frequent terms.
    • Discriminant analysis of sub-tree centroids.
the biggest issue in clustering
The biggest issue in clustering?
  • How do you compare two alternatives?
  • Computation (time/space) is only one metric of performance
  • How do you look at the “goodness” of the clustering produced by a method
resources
Resources
  • Scatter/Gather: A Cluster-based Approach to Browsing Large Document Collections (1992)
    • Cutting/Karger/Pederesen/Tukey
    • http://citeseer.nj.nec.com/cutting92scattergather.html
  • Data Clustering: A Review (1999)
    • Jain/Murty/Flynn
    • http://citeseer.nj.nec.com/jain99data.html
acknowledgments
Acknowledgments
  • Many of these slides were borrowed, from
    • Chris Manning’s Stanford module
    • Tan, Steinbach and Kumar, Intro to Data Mining
    • Ray Mooney’s Austin module