Presenter jhou yu liang authors shady shehata fakhri karray mohamed s kamel fellow 2012 ieee
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An Efficient Concept-Based Mining Model for Enhancing Text Clustering. Presenter : JHOU, YU-LIANG Authors :Shady Shehata , Fakhri Karray , Mohamed S. Kamel , Fellow 2012 , IEEE. Outlines. Motivation Objectives Methodology Evaluation Conclusions Comments. Motivation.

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Presenter jhou yu liang authors shady shehata fakhri karray mohamed s kamel fellow 2012 ieee

An Efficient Concept-Based Mining Model for Enhancing Text Clustering

Presenter : JHOU, YU-LIANGAuthors :Shady Shehata, FakhriKarray, Mohamed S. Kamel, Fellow2012, IEEE


Outlines
Outlines Clustering

  • Motivation

  • Objectives

  • Methodology

  • Evaluation

  • Conclusions

  • Comments


Motivation
Motivation Clustering

  • In text mining ,the term frequency is computed to explore the importance of the term in document.

  • However, two terms can have the same frequency in documents, but one term contributes more to the meaning of its sentences than the other term.


Objectives
Objectives Clustering

Using Concept-Based Mining Model for Text Clustering , improve the clustering quality.


Methodology concept based mining model
Methodology ClusteringConcept-Based Mining Model


Methodology concept based mining model1
Methodology ClusteringCONCEPT-BASED MINING MODEL

Ex:

a concept cwhich appears twice in document d in the first and the secondsentences The concept c appears fivetimes in the verb argument structures of the first sentence s 1 , and three times in the verb argument structures

of the second sentence s 2 .

ans : ctf value = (5+3)/2=4


Methodology corpus based concept analysis algorithm
Methodology ClusteringCorpus-Based Concept Analysis Algorithm


Methodology example of conceptual term frequency
Methodology ClusteringExample of Conceptual Term Frequency

. [ARG0 Texas and Australia researchers] have [TARGET created] [ARG1 industry-ready sheets of materials made from nanotubes that could lead to

the development of artificial muscles].

[ARG1 materials] [TARGET made ] [ARG2 from nanotubes that could lead

to the development of artificial muscles].

[ARG1 nanotubes] [R-ARG1 that] [ARGM-MOD could] [TARGET lead] [ARG2 to the development of artificial muscles].


Methodology example of conceptual term frequency1
Methodology ClusteringExample of Conceptual Term Frequency

1. First verb argument structure for the verb created:

. [ARG0 Texas and Australia researchers]

. [TARGET created]

. [ARG1 industry-ready sheets of materials made

from nanotubes that could lead to the development of artificial muscles].

2. Second verb argument structure for the verb made:

. [ARG1 materials]

. [TARGET made]

. [ARG2 from nanotubes that could lead to the development of artificial muscles].

3. Third verb argument structure for the verb lead:

. [ARG1 nanotubes]

. [R-ARG1 that]

. [ARGM-MOD could]

. [TARGET lead]

. [ARG2 to the development of artificial muscles].


Methodology example of conceptual term frequency2
Methodology ClusteringExample of Conceptual Term Frequency

1. Concepts in the first verb argument structure of the verb created:

. Texas Australia researchers

. created

. industry-ready sheets materials nanotubes lead development artificial muscles

2. Concepts in the second verb argument structure of the verb made:

. materials

. nanotubes lead development artificial muscles

3. Concepts in the third verb argument structure of the verb lead:

. nanotubes

. lead

. development artificial muscles.


Methodology example of conceptual term frequency3
Methodology ClusteringExample of Conceptual Term Frequency


Methodology concept based similarity measure
Methodology ClusteringConcept-Based Similarity Measure






Conclusions
Conclusions Clustering

The new approach enhance text clustering quality.


Comments
Comments Clustering

Advantages

Improve the text clustering quality.

Applications

-Concept-based mining model

-Conceptual term frequency


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