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Self-organizing map vs initial centroid selection optimization to enhance K-means with genetic algorithm to cluster transcribed broadcast news documents
Joint Authors
Badr, Amr
Maghawiri, Ahmad
Umar, Yasir
Source
The International Arab Journal of Information Technology
Issue
Vol. 17, Issue 3 (31 May. 2020), pp.316-324, 9 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2020-05-31
Country of Publication
Jordan
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
A compilation of artificial intelligence techniques are employed in this research to enhance the process of clustering transcribed text documents obtained from audio sources.
Many clustering techniques suffer from drawbacks that may cause the algorithm to tend to sub optimal solutions, handling these drawbacks is essential to get better clustering results and avoid sub optimal solutions.
The main target of our research is to enhance automatic topic clustering of transcribed speech documents, and examine the difference between implementing the K-means algorithm using our Initial Centroid Selection Optimization (ICSO) [16] with genetic algorithm optimization with Chi-square similarity measure to cluster a data set then use a self-organizing map to enhance the clustering process of the same data set, both techniques will be compared in terms of accuracy.
The evaluation showed that using K-means with ICSO and genetic algorithm achieved the highest average accuracy.
American Psychological Association (APA)
Maghawiri, Ahmad& Umar, Yasir& Badr, Amr. 2020. Self-organizing map vs initial centroid selection optimization to enhance K-means with genetic algorithm to cluster transcribed broadcast news documents. The International Arab Journal of Information Technology،Vol. 17, no. 3, pp.316-324.
https://search.emarefa.net/detail/BIM-962340
Modern Language Association (MLA)
Maghawiri, Ahmad…[et al.]. Self-organizing map vs initial centroid selection optimization to enhance K-means with genetic algorithm to cluster transcribed broadcast news documents. The International Arab Journal of Information Technology Vol. 17, no. 3 (May. 2020), pp.316-324.
https://search.emarefa.net/detail/BIM-962340
American Medical Association (AMA)
Maghawiri, Ahmad& Umar, Yasir& Badr, Amr. Self-organizing map vs initial centroid selection optimization to enhance K-means with genetic algorithm to cluster transcribed broadcast news documents. The International Arab Journal of Information Technology. 2020. Vol. 17, no. 3, pp.316-324.
https://search.emarefa.net/detail/BIM-962340
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 323-324
Record ID
BIM-962340