Ontology-based Text Document Clustering

Author Topic: Ontology-based Text Document Clustering  (Read 243 times)

Offline Nazia Nishat

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Ontology-based Text Document Clustering
« on: March 29, 2019, 01:16:10 AM »
Text  clustering  typically  involves  clustering  in  a  high  dimensional  space,  which  appears  difficult  with  regard  to  virtually  all  practical  settings.  In  addition,  given  a  particular  clustering  result it is typically very hard to come up with a good explanation of why the text clusters have been  constructed  the  way  they  are.  In  this  paper,  we  propose  a  new  approach  for  applying  background knowledge during preprocessing in order to improve clustering results and allow for selection between results. We preprocess our input data applying an ontology-based heuristics for feature  selection  and  feature  aggregation.  Thus,  we  construct  a  number  of  alternative  text  representations. Based on these representations, we compute multiple clustering results using K-Means.  The  results  may  be  distinguished  and  explained  by  the  corresponding  selection  of  concepts   in   the   ontology.   Our   results   compare   favourably   with   a   sophisticated   baseline   preprocessing strategy.

Link: https://www.kde.cs.uni-kassel.de/wp-content/uploads/benz/hotho/pub/Ontology_based_Text_Document_Clustering_2002.pdf

Offline s.arman

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Re: Ontology-based Text Document Clustering
« Reply #1 on: April 17, 2019, 03:12:33 PM »
Thanks for sharing

Offline khalid

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Re: Ontology-based Text Document Clustering
« Reply #2 on: April 19, 2019, 12:01:11 AM »
helpful

Offline lamisha

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Re: Ontology-based Text Document Clustering
« Reply #3 on: July 10, 2019, 09:34:42 AM »
Informative post madam