COMPARING K-MEANS CLUSTERS ON PARALLEL PERSIAN-ENGLISH CORPUS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

KHAZAEI A.; GHASEMZADEH M.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING Year:2015 | Volume:3 | Issue:2 Page(s): 203-208

Download Full-Text

Persian Verion

View:

736

Download:

182

Cites:

Information Journal Paper

Title

COMPARING K-MEANS CLUSTERS ON PARALLEL PERSIAN-ENGLISH CORPUS

Author(s)

KHAZAEI A. | GHASEMZADEH M. | Issue Writer Certificate

Keywords

CLUSTERINGQ2

MIZAN ENGLISH-PERSIAN PARALLEL CORPUSQ2

K-MEANSQ2

PRINCIPAL COMPONENT ANALYSIS (PCA)Q2

Abstract

This paper compares clusters of aligned Persian and English texts obtained from K-MEANS method. Text CLUSTERING has many applications in various fields of natural language processing. So far, much English documents CLUSTERING research has been accomplished. Now this question arises, are the results of them extendable to other languages? Since the goal of document CLUSTERING is grouping of documents based on their content, it is expected that the answer to this question is yes. On the other hand, many differences between various languages can cause the answer to this question to be no. This research has focused on kmeans that is one of the basic and popular document CLUSTERING methods. We want to know whether the clusters of aligned Persian and English texts obtained by the K-MEANS are similar. To find an answer to this question, MIZAN ENGLISH-PERSIAN PARALLEL CORPUS was considered as benchmark. After features extraction using text mining techniques and applying the PCA dimension reduction method, the K-MEANS CLUSTERING was performed. The morphological difference between English and Persian languages caused the larger feature vector length for Persian. So almost in all experiments, the English results were slightly richer than those in Persian. Aside from these differences, the overall behavior of Persian and English clusters was similar. These similar behaviors showed that results of K-MEANS research on English can be expanded to Persian. Finally, there is hope that despite many differences between various languages, CLUSTERING methods may be extendable to other languages.

Cites

No record.

References

No record.

Cite

APA: Copy

KHAZAEI, A., & GHASEMZADEH, M.. (2015). COMPARING K-MEANS CLUSTERS ON PARALLEL PERSIAN-ENGLISH CORPUS. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, 3(2), 203-208. SID. https://sid.ir/paper/255378/en

Vancouver: Copy

KHAZAEI A., GHASEMZADEH M.. COMPARING K-MEANS CLUSTERS ON PARALLEL PERSIAN-ENGLISH CORPUS. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING[Internet]. 2015;3(2):203-208. Available from: https://sid.ir/paper/255378/en

IEEE: Copy

A. KHAZAEI, and M. GHASEMZADEH, “COMPARING K-MEANS CLUSTERS ON PARALLEL PERSIAN-ENGLISH CORPUS,” JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, vol. 3, no. 2, pp. 203–208, 2015, [Online]. Available: https://sid.ir/paper/255378/en

Related Journal Papers