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Measuring Contribution of HTML Features in WebDocument Clustering

Oldemar Rodríguez

School of Mathematics, UCR

and Predisoft

Esteban Meneses

Computing Research Center, ITCR

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Motivation

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Motivation

Which HTML feature is the most important to provide good clustering results?

Using symbolic objects to cluster web documents.

15th World Wide Web Conference (2006)

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HTML Document Clustering

Find meaningful groups from a web document collection.

Effectively represent web document clusters for further analysis.

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HTML Document

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Classical Representations

• Different approaches for representing a web document.

<5,22,19,4,...,38>

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Vectorial Representation

• Every document is represented by a vector inn-dimensional space.

• Bag of words scheme. Each variable represents the relative weight of a term in the document.

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Symbolic Objects

• Real-life objects are too complex to be represented by points in a vectorial space. [Bock&Diday, 2000]

• Symbolic objects overcome this limitation by representing concepts rather than individuals.

• In a symbolic data array each variable can have one of many data types: sets, intervals, histograms, trees, graphs, functions, fuzzy data, etc.

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Symbolic Data Table

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Multivariate Numeric Analysis

Individual Age Profession Wage Location

3457 36 Lawyer 2,500.00 San José

1251 28 Teacher 1,750.00 Alajuela

3245 39 Doctor 2,400.00 San José

7635 33 Teacher 1,900.00 Alajuela

3245 35 Engineer 1,850.00 Alajuela

5367 27 Engineer 1,900.00 Heredia

6486 34 Manager 1,600.00 Heredia

Individual Age Profession Wage

San José [36,39] {Law, 50%,Doc,50%} [2,4 – 2,5]

Alajuela [28,35] {Tea,66%,Eng,33%} [1,75 – 1,9]

Heredia [2,34] {Eng,50%,Mgn,50%} [1,6 – 1,9]

Multivariate Symbolic Analysis

Millions…

Hundreds…

Data

Concepts

From relational data bases to symbolic data bases

Symbolic Data Table

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Relational Data Base Symbolic Data Base

100% knowledge

15 Gigabyte

90 % knowledge

10.3 Megabyte

Symbolic Data Base

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Symbolic Representations

• A complex representation that takes into account: term frequency, word order and phrases.

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The K-Means Clustering Method

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But, there are some problems …….

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Distance Measures

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Teorema: Igualdad de Fisher

• Inercia total Inercia total = Inercia inter-clases Inercia inter-clases

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Inercia intra-clasesInercia intra-clases

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1. Representar una clase por su centro de gravedad, esto es, por su vector de promedios.

2. ¿Qué es el centro de gravedad?

Problemas en el caso simbólico:

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¿Qué el centro de gravedad?

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Evaluation Criteria

1. Rand Index

2. Mutual Information

3. F-Measure

4. Entropy

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Experiments

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Experiments

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Experiments

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Experiments

Text 0.2894

Title 0.2584

Bold 0.0379

Anchor 0.1689

Header 0.1009

Graph 0.1229

Tree 0.0212

WebKB

Text 0.7035

Graph 0.2515

Tree 0.0449

20Newsgroup

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Conclusions

• Symbolic representations are richer and more flexible than classical representations.

• The text in the HTML document seems to be the more important factor to cluster HTML documents.

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Thank you!