Lahbib Ajallouda
ABSTRACT
Today, we are witnessing rapid growth in Web resources that allow Internet users to express and share their ideas, opinions, and judgments on a variety of issues. Several classification approaches have been proposed to classify textual data. But all these approaches require us to label the clusters we want to obtain. Which, in reality, is not available because we do not know in advance the information that can be proposed through these opinions. To overcome this constraint, clustering approaches such as K-mean, HAC or FCM can be exploited. In this paper, we present and compare these approaches. And to show the importance of exploiting clustering algorithms, to classify and analyze textual data in Arabic. By applying them to a real case that has created a great debate in Morocco, which is the case of teachers contracting with academies.
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Index Terms
- K-means, HAC and FCM Which Clustering Approach for Arabic Text?
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