Using self organizing map to cluster Arabic crime documents

Meshrif Alruily; Aladdin Ayesh; Abdulsamad Al-Marghilani

doi:10.1109/imcsit.2010.5679616

Using self organizing map to cluster Arabic crime documents

Meshrif Alruily^*, Aladdin Ayesh, Abdulsamad Al-Marghilani

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

15 Citations (Scopus)

Abstract

This paper presents a system that combines two text mining techniques; information extraction and clustering. A rulebased approach is used to perform the information extraction task, based on the dependency relation between some intransitive verbs and prepositions. This relationship helps in extracting types of crime from documents within the crime domain. With regard to the clustering task, the Self Organizing Map (SOM) is used to cluster Arabic crime documents based on crime types. This work is then validated through experiments, the results of which show that the techniques developed here are promising.

Original language	English
Title of host publication	Proceedings of the International Multiconference on Computer Science and Information Technology, IMCSIT 2010
Publisher	IEEE Computer Society
Pages	357-363
Number of pages	7
ISBN (Print)	9788360810279
DOIs	https://doi.org/10.1109/imcsit.2010.5679616
Publication status	Published - 2010

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Using self organizing map to cluster Arabic crime documents. / Alruily, Meshrif; Ayesh, Aladdin; Al-Marghilani, Abdulsamad.

Proceedings of the International Multiconference on Computer Science and Information Technology, IMCSIT 2010. IEEE Computer Society, 2010. p. 357-363 5679616.

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

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AB - This paper presents a system that combines two text mining techniques; information extraction and clustering. A rulebased approach is used to perform the information extraction task, based on the dependency relation between some intransitive verbs and prepositions. This relationship helps in extracting types of crime from documents within the crime domain. With regard to the clustering task, the Self Organizing Map (SOM) is used to cluster Arabic crime documents based on crime types. This work is then validated through experiments, the results of which show that the techniques developed here are promising.

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Using self organizing map to cluster Arabic crime documents

Abstract

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