Flat Clustering

Prasad L16FlatCluster 1

Flat Clustering

Adapted from Slides by

Prabhakar Raghavan, Christopher Manning,

Ray Mooney and Soumen Chakrabarti

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Today’s Topic: Clustering

Document clustering Motivations Document representations Success criteria

Clustering algorithms Partitional (Flat) Hierarchical (Tree)

Prasad L16FlatCluster

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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


A data set with clear cluster structure

How would you design an algorithm for finding the three clusters in this case?

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Classification vs. Clustering

Classification: supervised learningClustering: unsupervised learningClassification: Classes are human-defined and part of the input to the learning algorithm.Clustering: Clusters are inferred from the data without human input.

However, there are many ways of influencing the outcome of clustering: number of clusters, similarity measure, representation of documents, . . .

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Applications of clustering in IR

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Application What isclustered?

Benefit

Search result clustering

searchresults

more effective informationpresentation to user

Scatter-Gather (subsetsof) collection

alternative user interface: “search without typing”

Collection clustering collection effective informationpresentation for exploratory browsing

Cluster-based retrieval

collection higher efficiency:faster search

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Yahoo! Hierarchy isn’t clustering but is the kind of output you want from clustering

dairycrops

agronomyforestry

AI

HCIcraft

missions

botany

evolution

cellmagnetism

relativity

courses

agriculture biology physics CS space

... ... ...

… (30)

www.yahoo.com/Science

... ...


Introduction to Information RetrievalIntroduction to Information Retrieval

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Global navigation: Yahoo

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Global navigation: MESH (upper level)

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Global navigation: MESH (lower level)

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Navigational hierarchies: Manual vs. automatic creation

Note: Yahoo/MESH are not examples of clustering.But they are well known examples for using a global hierarchy for navigation.Some examples for global navigation/exploration based on clustering:

CartiaThemescapesGoogle News

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Search result clustering for better navigation

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Google News: automatic clustering gives an effective news presentation metaphor

Selection Metrics Google News taps into its own unique

ranking signals, which include user clicks, the estimated authority of a

publication in a particular topic (possibly taking location into account),

Freshness/recency, geography and more.


Scatter/Gather: Cutting, Karger, and Pedersen

Sca

tter

/Gat

her

(S

catt

er/G

ath

er ( “

“ Sta

rS

tar ””

))

S/G Example: query on S/G Example: query on ““starstar””

Encyclopedia textEncyclopedia text14 sports14 sports

8 symbols8 symbols 47 film, tv47 film, tv 68 film, tv (p)68 film, tv (p) 7 music 7 music97 astrophysics97 astrophysics 67 astronomy(p)67 astronomy(p) 12 steller phenomena12 steller phenomena 10 flora/fauna10 flora/fauna 49 galaxies, stars 49 galaxies, stars

29 constellations29 constellations 7 miscelleneous7 miscelleneous

Clustering and Clustering and re-clusteringre-clustering is entirely automated is entirely automated

Scatter/GatherScatter/GatherCutting, Pedersen, Tukey & Karger 92, 93, Hearst & Cutting, Pedersen, Tukey & Karger 92, 93, Hearst &

Pedersen 95Pedersen 95

• How it worksHow it works– Cluster sets of documents into general

“themes”, like a table of contents – Display the contents of the clusters by

showing topical terms and typical titles– User chooses subsets of the clusters

and re-clusters the documents within – Resulting new groups have different

“themes”

For visualizing a document collection and its themes

Wise et al, “Visualizing the non-visual” PNNL ThemeScapes, Cartia

[Mountain height = cluster size]

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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 Example: The query “car” will also return docs

containing automobile Because clustering grouped together docs

containing car with those containing automobile.

Why might this happen?Prasad

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Issues for clustering

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


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What makes docs “related”?

Ideal: semantic similarity. Practical: statistical similarity

Docs as vectors. For many algorithms, easier to think

in terms of a distance (rather than similarity) between docs.

We can use cosine similarity (alternatively, Euclidean Distance).


More Applications of clustering … Image Processing

Cluster images based on their visual content Web

Cluster groups of users based on their access patterns on webpages

Cluster webpages based on their content Bioinformatics

Cluster similar proteins together (similarity w.r.t. chemical structure and/or functionality etc.)

…

Outliers Outliers are objects that do not belong to any cluster or

form clusters of very small cardinality

In some applications we are interested in discovering outliers, not clusters (outlier analysis)

cluster

outliers

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Clustering Algorithms

Partitional (Flat) algorithms Usually start with a random (partial)

partition Refine it iteratively

K means clustering (Model based clustering)

Hierarchical (Tree) 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

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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: exhaustively enumerate all

partitions Effective heuristic methods: K-means and K-

medoids algorithms


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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)


cx

xc

||

1(c)μ

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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. (Update the seeds to the centroid of each cluster) For each cluster cj

sj = (cj) Prasad L16FlatCluster

K-means algorithm

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K Means Example(K=2)

Pick seeds

Reassign clusters

Compute centroids

xx

Reassign clusters

xx xx Compute centroids

Reassign clusters

Converged!


Worked Example: Set to be clustered

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Worked Example: Random selection of initial centroids

Exercise: (i) Guess what the optimal clustering into two clusters is in this case; (ii) compute the centroids of the clusters

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Worked Example: Assign points to closest center

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Worked Example: Assignment

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Worked Example: Recompute cluster centroids

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Worked Example: Assign points to closest centroid

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Worked Example: Recompute cluster caentroids

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Worked Example: Centroids and assignments after convergence

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Two different K-means Clusterings

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

0

0.5

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x

y

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

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0.5

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Sub-optimal Clustering

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

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Optimal Clustering

Original Points

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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?

Prasad

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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.


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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

Intuition: Reassignment monotonically decreases G since each vector is assigned to the closest centroid.

Lower case


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

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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).


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Optimality of K-means

Convergence does not mean that we converge to the optimal clustering!This is the great weakness of K-means.If we start with a bad set of seeds, the resulting clustering can be horrible.

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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 startwith B and E as centroidsyou converge to {A,B,C}and {D,E,F}.If you start with D and Fyou converge to {A,B,D,E} {C,F}.

Example showingsensitivity to seeds


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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.


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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 E.g., for query results - ideal value of K not known

up front - though UI may impose limits.


Prasad L17HierCluster 75

Hierarchical Clustering

Adapted from Slides by Prabhakar Raghavan, Christopher Manning,

Ray Mooney and Soumen Chakrabarti

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“The Curse of Dimensionality”

Why document clustering is difficult While clustering looks intuitive in 2 dimensions,

many of our applications involve 10,000 or more dimensions…

High-dimensional spaces look different The probability of random points being close drops

quickly as the dimensionality grows. Furthermore, random pair of vectors are all almost

perpendicular.

Prasad L17HierCluster

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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


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• Clustering obtained by cutting the dendrogram at a desired level: each connectedconnected component forms a cluster.

Dendogram: Hierarchical Clustering


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Agglomerative (bottom-up): Precondition: Start with each document as a separate

cluster. Postcondition: Eventually all documents belong to the

same cluster. Divisive (top-down):

Precondition: Start with all documents belonging to the same cluster.

Postcondition: Eventually each document forms a cluster of its own.

Does not require the number of clusters k in advance.

Needs a termination/readout condition

Hierarchical Clustering algorithms

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Hierarchical agglomerative clustering (HAC)

HAC creates a hierarchy in the form of a binary tree.Assumes a similarity measure for determining the similarity of two clusters.

Up to now, our similarity measures were for documents.

We will look at four different cluster similarity measures.

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Hierarchical Agglomerative Clustering (HAC) Algorithm

Start with all instances in their own cluster.Until there is only one cluster: Among the current clusters, determine the two clusters, ci and cj, that are most similar. Replace ci and cj with a single cluster ci cj


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Dendrogram: Document Example

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

d1

d2

d3

d4

d5

d1,d2 d4,d5 d3

d3,d4,d5


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Key notion: cluster representative

We want a notion of a representative point in a cluster, to represent the location of each 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. point that is the “average” of all docs in the cluster

Centroid or center of gravity Measure inter-cluster distances by distances of centroids.


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

d1 d2

d3

d4

d5

d6

Centroid after first step.

Centroid aftersecond step.

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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.

CentroidOutlier

Say 10.



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Key question: How to define cluster similarity Single-link: Maximum similarity

Maximum similarity of any two documents Complete-link: Minimum similarity

Minimum similarity of any two documents Centroid: Average “intersimilarity”

Average similarity of all document pairs (but excluding pairs of docs in the same cluster)

This is equivalent to the similarity of the centroids. Group-average: Average “intrasimilarity”

Average similarty of all document pairs, including pairs of docs in the same cluster

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Cluster similarity: Example

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Single-link: Maximum similarity

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Complete-link: Minimum similarity

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Centroid: Average intersimilarityintersimilarity = similarity of two documents in different clusters

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Group average: Average intrasimilarityintrasimilarity = similarity of any pair, including cases where thetwo documents are in the same cluster

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Cluster similarity: Larger Example

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Single-link: Maximum similarity

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Complete-link: Minimum similarity

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Centroid: Average intersimilarity

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Group average: Average intrasimilarity

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Single Link Agglomerative Clustering

Use maximum similarity of pairs:

After merging ci and cj, the similarity of the resulting cluster to another cluster, ck, is:

),(max),(,

yxsimccsimji cycx

ji

)),(),,(max()),(( kjkikji ccsimccsimcccsim


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Single Link Example



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Single-link: Chaining

Single-link clustering often produces long, straggly clusters. For most applications, these are undesirable.

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Complete Link Agglomerative Clustering

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),(,

yxsimccsimji cycx

ji

)),(),,(min()),(( kjkikji ccsimccsimcccsim

Ci Cj CkPrasad

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Complete Link Example



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Complete-link: Sensitivity to outliers

The complete-link clustering of this set splits d2 from its right neighbors – clearly undesirable.

The reason is the outlier d1. This shows that a single outlier can negatively affect the

outcome of complete-link clustering. Single-link clustering does better in this case.

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Computational Complexity In the first iteration, all HAC methods need to

compute similarity of all pairs of n individual instances which is O(n2).

In each of the subsequent n2 merging iterations, it computes 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 cluster must be done in constant time. Often O(n3) if done naively or O(n2 log n) if done

cleverly Prasad L17HierCluster

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Group Average Agglomerative Clustering

Use average similarity across all pairs within the merged cluster to measure the similarity of two clusters.

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),(

ji jiccx xyccyjiji

ji yxsimcccc

ccsim


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Computing Group Average Similarity

Assume cosine similarity and normalized vectors with unit length.

Always maintain sum of vectors in each cluster.

Compute similarity of clusters in constant time:

jcx

j xcs

)(

)1||||)(|||(|

|)||(|))()(())()((),(

jiji

jijijiji cccc

cccscscscsccsim


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Medoid as Cluster Representative

The centroid does not have to be a document. Medoid: A cluster representative that is one of

the documents (E.g., the document closest to the centroid)

One reason this is useful Consider the representative of a large cluster (>1K docs)

The centroid of this cluster will be a dense vector The medoid of this cluster will be a sparse vector

Compare: mean/centroid vs. median/medoid


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Efficiency: “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

Prasad

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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 “Animal” or “Car” in

the jaguar example. In topic trees (Yahoo), need navigational

cues. Often done by hand, a posteriori.


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How to Label Clusters

Show titles of typical documents Authors create titles for quick scanning! But you can only show a few titles which may not

fully represent cluster E.g., Our NLDB-08 paper synthesizes titles for News

clusters

Show words/phrases prominent in cluster More likely to fully represent cluster (tag cloud) Use distinguishing words/phrases

Differential/contrast labeling


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Labeling

Common heuristics - list 5-10 most frequent terms in the centroid vector. Drop stop-words; stem.

Differential labeling by frequent terms Within a collection “Computers”, clusters all have

the word computer as frequent term. Discriminant analysis of centroids.

Perhaps better: distinctive noun phrase


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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


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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.


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External Evaluation of Cluster Quality

Simple measure: purity, the ratio between the dominant class in the cluster πi and the size of cluster ωi

Others are entropy of classes in clusters (or mutual information between classes and clusters)

Cjnn

Purity ijji

i )(max1

)(


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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

Rand Index

Number of points

Same cluster in clustering

Different clusters in clustering

Same class in ground truth A C

Different classes in ground truth

B D

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Rand index: symmetric version

BA

AP

DCBA

DARI

CA

AR

Compare with standard Precision and Recall.


Rand Index example: 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

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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, . . .

Flat Clustering

Documents

Transcript of Flat Clustering