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Comparisons between data clustering algorithms
Author
Source
The International Arab Journal of Information Technology
Issue
Vol. 5, Issue 3 (31 Jul. 2008), pp.320-325, 6 p.
Publisher
Publication Date
2008-07-31
Country of Publication
Jordan
No. of Pages
6
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
Clustering is a division of data into groups of similar objects.
Each group, called a cluster, consists of objects that are similar between themselves and dissimilar compared to objects of other groups.
This paper is intended to study and compare different data clustering algorithms.
The algorithms under investigation are : k-means algorithm, hierarchical clustering algorithm, self-organizing maps algorithm, and expectation maximization clustering algorithm.
All these algorithms are compared according to the following factors : size of dataset, number of clusters, type of dataset and type of software used.
Some conclusions that are extracted belong to the performance, quality, and accuracy of the clustering algorithms.
American Psychological Association (APA)
Abu Abbas, Usamah. 2008. Comparisons between data clustering algorithms. The International Arab Journal of Information Technology،Vol. 5, no. 3, pp.320-325.
https://search.emarefa.net/detail/BIM-11511
Modern Language Association (MLA)
Abu Abbas, Usamah. Comparisons between data clustering algorithms. The International Arab Journal of Information Technology Vol. 5, no. 3 (Jul. 2008), pp.320-325.
https://search.emarefa.net/detail/BIM-11511
American Medical Association (AMA)
Abu Abbas, Usamah. Comparisons between data clustering algorithms. The International Arab Journal of Information Technology. 2008. Vol. 5, no. 3, pp.320-325.
https://search.emarefa.net/detail/BIM-11511
Data Type
Journal Articles
Language
English
Notes
includes bibliographical references : p. 25
Record ID
BIM-11511