Abdessalem Kelaiaia

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Recently, probabilistic topic models such as Latent Dirichlet Allocation (LDA) have been widely used for applications in many text mining tasks such as retrieval, summarization and clustering on different languages. In this paper, we present a first comparative study between LDA and K-means, two well-known methods respectively in topics identification and(More)
Recently, probabilistic topic models such as Latent Dirichlet Allocation (LDA) have been widely used for applications in many text mining tasks such as retrieval, summarization and clustering on different languages. In this paper, we present a first comparative study between LDA and K-means, two well-known methods respectively in topics identification and(More)
Initially, this paper, sets out to study the influence of stemming on the quality of the Arabic text clustering, and then describes the testing the application of an approach based on this clustering to improve Document Retrieval (DR). A classical local document system generally, employs statistical methods for calculating the similarity between the(More)
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