A fuzzy approach based for document datasets clustering

Joint Authors

Hadi, Raghad Muhammad
Mawlud, Abir Tariq
Hashim, Sukaynah Hasan

Source

Journal of College of Education for Pure Sciences

Issue

Vol. 9, Issue 1 (31 Mar. 2019), pp.1-13, 13 p.

Publisher

University of Thi-Qar College of Education for Pure Sciences

Publication Date

2019-03-31

Country of Publication

Iraq

No. of Pages

13

Main Subjects

Information Technology and Computer Science

Topics

Abstract EN

In large Computers; the huge volume of files actually generate disorder to analyze it.

So, it desires to design a clustering techniques which reduce the costs of analysts.

Document clustering is an essential process in text mining, which retrieve the information with an acceptable accuracy, which can be achieved by fuzzy clustering.

Reuters 21578 dataset is used for experimental purpose; the proposed system was tested by using Reuters 21578 datasets according to the time required to cluster data.

The proposed system improves data clustering algorithms by construct required fuzzy clusters.

The proposed system showed a good result compared with clustering techniques in comparing with other clustering techniques in time efficiency.

American Psychological Association (APA)

Hadi, Raghad Muhammad& Hashim, Sukaynah Hasan& Mawlud, Abir Tariq. 2019. A fuzzy approach based for document datasets clustering. Journal of College of Education for Pure Sciences،Vol. 9, no. 1, pp.1-13.
https://search.emarefa.net/detail/BIM-891561

Modern Language Association (MLA)

Hadi, Raghad Muhammad…[et al.]. A fuzzy approach based for document datasets clustering. Journal of College of Education for Pure Sciences Vol. 9, no. 1 (Mar. 2019), pp.1-13.
https://search.emarefa.net/detail/BIM-891561

American Medical Association (AMA)

Hadi, Raghad Muhammad& Hashim, Sukaynah Hasan& Mawlud, Abir Tariq. A fuzzy approach based for document datasets clustering. Journal of College of Education for Pure Sciences. 2019. Vol. 9, no. 1, pp.1-13.
https://search.emarefa.net/detail/BIM-891561

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references : p. 12-13

Record ID

BIM-891561