K-Means Algorithm for Clustering Afaan Oromo Text Documents using Python Tools
Naol Bakala

Naol Bakala*, Department of Computer Science, Ambo University, Ambo, Ethiopia. 

Manuscript received on April 02, 2020. | Revised Manuscript received on April 21, 2020. | Manuscript published on May 30, 2020. | PP: 1279-1282 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2284059120/2020©BEIESP | DOI: 10.35940/ijrte.A2284.059120
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: With the advancement of technology and proliferation of computers in the country, the amount of Afaan Oromo language news documents produced increasingly which becomes a difficult task for news agencies to organize such huge collection of documents items manually. To solve this problem, researches is conducted using unsupervised machine learning python tools for Afaan Oromo news document clustering with low cost and best quality of clustering solution. In this research work focusing on k-means clustering analysis which produced better results as compared to the other cluster analysis both in terms of time requirement and the quality of the clusters produced. 
Keywords: Afaan Oromo Language, Afaan Oromo Text Document, Kmeans, cluster, Clustering Model.
Scope of the Article: Clustering