extending the growing hierarchal som for clustering documents in graphs domain

Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

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Extending the Growing Hierarchal SOM for Clustering Documents in Graphs domain

Presenter : Cheng-Hui Chen　Authors : Mahmoud F. Hussin, Mahmoud R. farra and Yasser El-Sonbaty

IJCNN, 2008


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Outlines Motivation Objectives Methodology Experiments Conclusions Comments


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I. M.Motivation· The variants of SOM are limited by the fact that they

use only the VSM for document representations. ─ It does not represent any relation between the words.─ The space complexity to the VSM.

· The sentences being broken down into their individual components without any representation of the sentence structure.

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term B

term A

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D1

D2

θ

d

river raftingmild


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I. M.Objectives· Using graphs to represent documents helped

the salient features of data through using edges to represent relations and using vertices to represent words.

· The decrease the space complexity comparing to the VSM.

· The extend the GHSOM to work in the graph domain to enhance the quality of clusters.

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rafting

river

mild


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I. M.Methodology

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I. M.Enhance the DIG to work with G-GHSOM· The Document Index Graph (DIG) model

─ the DIG for representing the document and Exploited it in the document clustering.

· For example (the document table of word "river" is shown)─ River rafting. (doc1)─ mild river rafting. (doc2)─ River fishing. (doc3)

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e1 S0(1)

e0 S0(0)

river

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I. M.Enhance the DIG to work with G-GHSOM

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I. M.Enhance the DIG to work with G-GHSOM· Single-word similarity measure

· Two document vectors similarity measure

· The total similarity is the integration

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I. M.Ment of the G-GHSOM to work with graph· Neuron Initialization

─ Detecting the matching list to calculate the phrase based similarity.

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I. M.Ment of the G-GHSOM to work with graph

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I. M.Ment of the G-GHSOM to work with graph

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Gin


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I. M.Experiments· Data set

─ Reuter’s news articles (RNA)─ University of Waterloo and Canadian Web sites (UW-

CAN)

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I. M.Experiments

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I. M.Conclusions· The extend the GHSOM to a new graph based

GHSOM: (G-GHSOM) to enhance the quality of the document clustering.─ G-GHSOM works successfully with graph domain and

achieves a better quality clustering than TGHSOM in document clustering.

· The enhanced the DIG model to work with GHSOM algorithm.

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Comments· Advantages

─ Enhance the quality of the document clustering· Application

─ SOM─ Clustering