2015/12/251 hierarchical document clustering using frequent itemsets benjamin c.m. fung, ke wangy...

112/04/21 1

Hierarchical Document Clustering Using Frequent Itemsets

Benjamin C.M. Fung, Ke Wangy and Martin Ester

Proceeding of International Conference on Data Mining, SIAM 2003

報告人 : 吳建良

Outline Hierarchical Document Clustering Proposed Approach

Frequent Itemset-based Hierarchical Clustering (FIHC)

Experimental Evaluation Conclusions

2

Hierarchical Document Clustering

Document Clustering Automatically organize documents into clusters Documents within a cluster have high similarity Documents within different clusters are very dissimilar

Hierarchical Document Clustering

3

Sports

Soccer Tennis

Tennis ball

Challenges in Hierarchical Document Clustering

High dimensionality. High volume of data Consistently high clustering quality. Meaningful cluster description

4

Overview of FIHC

5

PreprocessingDocuments

(High dimensional doc vectors) Generate

frequent itemsets

(Reduced dimensions feature vectors)

Construct clustersBuild a TreePruning

Cluster Tree

Preprocessing Remove stop words and Stemming Construct vector model

doci= ( item frequency1, if2, if3, …, ifm) EX:

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Generate Frequent Itemsets Use Agrawal et al. proposed algorithm to find global

frequent itemsets Minimum global support

a percentage of all documents Global frequent itemset

a set of items (words) that appear together in more than a minimum global support of the whole document set

Global frequent item an item that belongs to some global frequent itemset

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Reduced Dimensions Vector Model High dimensional vector model Set the minimum global support to 35% Store the frequencies only for global frequent items

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Construct Initial Clusters Construct a cluster for each global frequent itemset All documents containing this itemset are included in

the same cluster

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C(flow)

C(form)

C(layer)C(patient

)

cran.1cran.2cran.3cran.4cran.5

Its cluster label is {result}

C(result)

C(treatment)

C(flow, layer)

C(patient,treatment

)cisi.1cran.1cran.3med.2med.5


med.1med.2med.3med.4med.5med.6

cran.3med.1med.2med.4med.6

med.1med.2med.3med.4med.6



Cluster Frequent Items A global frequent item is cluster frequent in a cluster Ci if

the item is contained in some minimum fraction of documents in Ci

Suppose the minimum cluster support is set to 70%

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C(patient)

(flow, form, layer, patient, result, treatment)

med.1=( 0 0 0 8 1 2 )med.2=( 0 1 0 4 3 1 )med.3=( 0 0 0 3 0 2 )med.4=( 0 0 0 6 3 3 )med.5=( 0 1 0 4 0 0 )med.6=( 0 0 0 9 1 1 )

C(patient)

Item Cluster Support

form 33%

patient 100%

result 66%

treatment 83%

11Initial Cluster (minimum cluster support=70%)

Cluster Label vs. Cluster Frequent Items

Cluster label Use global frequent itemset as cluster label A set of mandatory items in the cluster Every document in the cluster must contain all the items in the

cluster label Used in hierarchical structure establishment

Cluster frequent items Appear in some minimum fraction of documents in the cluster Used in similarity measurement Topic description of the cluster

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Make Clusters Disjoint Initial clusters are not disjoint

Remove the overlapping of clusters Assign a document to the “best” initial cluster

Define the score function Score(Ci ← docj)

Measure the goodness of a cluster Ci for a document docj

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Score Function

Assign each docj to the initial cluster Ci that has the highest score

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)]()([)]()([)( x_supportglobalxnx_supportclusterxndocCScorexx

ji

• x represents a global frequent item in docj and the item is also cluster frequent in Ci

• x’ represents a global frequent item in docj but the item is not cluster frequent in Ci

• n(x) is the frequency of x in the feature vector of docj

• n(x’) is the frequency of x’ in the feature vector of docj

Score Function (cont.)

If the highest score are more than one Choose the one that has the most number of items in

the cluster label Key idea:

A cluster Ci is good for a document docj if there are many global frequent items in docj that appear in many documents in Ci

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Score Function - Example

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C(flow)

flow=100%layer=100%

C(form)

form=100%

C(layer)flow=100%layer=100%

C(patient)patient=100%

treatment=83%

C(result)result=100%

patient=80%

treatment=80%

C(treatment)

patient=100%

treatment=100%result=80%

C(flow, layer)flow=100%layer=100%

C(patient,treatment)patient=100%

treatment=100%result=80%


med.6=( 0 0 0 9 1 1 )

0+0-[(9 × 0.5)+(1 × 0.42)+(1 × 0.42)]= -5.34

-5.34 -5.34 9.41

10.6 10.8 -5.34

(9 × 1)+(1 × 1)+(1 × 0.8)= 10.8

global support of patient, result, and treatment

Recompute the Cluster Frequent Items

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• Recompute Ci, also include all descendants of Ci

• A descendant of Ci if its cluster label is a superset of the cluster label of Ci

none


med.5=( 0 1 0 4 0 0 )

• Consider C(patient)

C(patient). original


form 100%

patient 100%

C(patient). descendant


form 33%

patient 100%

result 66%

treatment 83%

Include descendant:C(patient, treatment)

Disjoint cluster result

Building the Cluster Tree Put the more specific clusters at the bottom of the tree Put the more general clusters at the top of the tree

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Building the Cluster Tree (cont.)

Tree level Level 0: root, mark “null” and store unclustered documents Level k: cluster label is global frequent k-itemset

Bottom-up manner Start from the cluster Ci with the largest number k of items in

its cluster label Identify all potential parents that are (k-1)-clusters and have

the cluster label being a subset of Ci’s cluster label Choose the “best” among potential parents

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Building the Cluster Tree (contd.)

The criterion for selecting the best Similar to choosing the best cluster for a document Method:

(1)Merge all the documents in the subtree of Ci into a single conceptual document doc(Ci)

(2)Compute the score of doc(Ci) against each potential parent Cj

The potential parent with the highest score would become the parent of Ci

All leaf clusters that contain no document can be removed

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))(( ij CdocCScore

Example Start from 2-cluster

C(flow, layer) and C(patient, treatment) C(flow, layer) is emptye remove C(patient, treatment)

Potential parents: C(patient) and C(treatment) C(treatment) is empty remove C(patient) gets a higher score and becomes

the parent of C(patient, treatment)

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null

C(flow)


C(form)

cisi.1

C(patient)

med.5

C(patient, treatment)


Prune Cluster Tree A small minimum global support

A cluster tree can be broad and deep Documents of the same topic are distributed over

several small clusters Poor clustering accuracy

The aim of tree pruning Produce a natural topic hierarchy for browsing Increase the clustering accuracy

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Inter-Cluster Similarity Inter_Sim of Ca and Cb

Reuse the score function to calculate Sim(Ci←Cj)

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2

1

)]()([)( abbaba CCSimCCSimCCSim_Inter

1)()(

))(()(

x x

jiji xnxn

CdocCScoreCCSim

Property of Sim(Ci←Cj)

Global support and cluster support are between 0 and 1 Maximum value of Score= , minimum is Normalize Score by , Sim is within -1~1 Avoid negative similarity value, add the term +1 The range of Sim function is 0~2, so is Inter_Sim Inter_Sim value is below 1

Weight of dissimilar items has exceeded the weight of similar items

A good threshold to distinguish two clusters24

)]()([)]()([)( x_supportglobalxnx_supportclusterxndocCScorexx

ji

xxn )(

x

xn )(

xxxnxn )()(

Child Pruning Objective: shorten the depth of a tree Procedure

1. Scan the tree in the bottom-up order

2. For each non-leaf node, calculate Inter_Sim between the node and each of its children

3. If Inter_Sim is above 1, prune the child cluster

4. If a cluster is pruned, its children become the children of their grandparent

Child pruning is only applicable to level 2

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Example Determine whether cluster C(patient, treatment) should be

pruned Compute the inter-cluster similarity between C(patient) and

C(patient, treatment) Sim(C(patient)←C(patient, treatment))

Combine all the documents in cluster C(patient, treatment) by adding up their feature vectors

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med.1=( 0 0 0 8 1 2 )med.2=( 0 1 0 4 3 1 )med.3=( 0 0 0 3 0 2 )med.4=( 0 0 0 6 3 3 )med.6=( 0 0 0 9 1 1 )

Sum = ( 0 1 0 30 8 9 )


70.11)81()930(

)42.0*842.0*1()83.0*91*30(

)),()((

treatmentpatientCpatientCSim

Example(cont.)

Sim(C(patient, treatment)←C(patient))=1.92 Inter_Sim(C(patient)↔C(patient, treatment))= Inter_Sim is above 1, cluster C(patient, treatment) is pruned

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81.1)92.1*70.1( 2

1

null

C(flow)


C(form)

cisi.1

C(patient)


Sibling Merging Sibling merging is applicable to level 1 Procedure

1. Calculate the Inter_Sim for each pair of clusters at level 1

2. Merge the cluster pair that has the highest Inter_Sim

Repeat above steps until1. User-specified number of clusters is reached,

or

2. All cluster pairs at level 1 have Inter_Sim below or equal to 1

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null

C(flow)

cran.1cran.2cran.3cran.4cran.5cisi.1

C(patient)


Experimental Evaluation Dataset

Clustering Quality (F-measure) Recall , Precision Corresponding F-measure: F-measure for whole clustering result:

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iij nnjiR ),( jij nnjiP ),(

Cluster j

Natural Class i

nij

Efficiency & Scalability

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Conclusions & Discussion This research exploits frequent itemsets for

Define a cluster Use score function, construct initial clusters, make disjoint clusters

Organize the cluster hierarchy Build cluster tree, prune cluster tree

Discussion: Use unordered frequent word sets

Different order of words may deliver different meaning

Multiple topics of documents

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