Abdelmalek Amine

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The increasing number of digitized texts presently available notably on the Web has developed an acute need in text mining techniques. Clustering systems are used more and more often in text mining, especially to analyze texts and to extract knowledge they contain. With the availability of the vast amount of clustering algorithms and techniques, it becomes(More)
The classification of textual documents has been widely studied. The majority of classification approaches use supervised learning methods, which are acceptable for rather small corpora allowing experts to generate representative sets of data for the training, but are not feasible for significant flows of data. Unsupervised classification methods discover(More)
With the great and rapidly growing number of documents available in digital form (Internet, library, CD-Rom…), the automatic classification of texts has become a significant research field and a fundamental task in document processing. This paper deals with unsupervised classification of textual documents also called text clustering using Self-Organizing(More)
This paper deals with our research on unsupervised classification for automatic language identification purpose. The study of this new hybrid algorithm shows that the combination of the Kmeans and the artificial ants and taking advantage of an n-gram text representation is promising. We propose an alternative approach to the standard use of both algorithms.(More)
A great number of methods of unsupervised classifications also called clustering were applied to the textual documents. In this paper, we initially propose the method of the self-organizing maps of Kohonen for the clustering of the textual documents based on the n-grams representation. The same method based on the synsets of WordNet as terms for the(More)
The classification of textual documents has been the subject of many studies. Technologies like the web and numerical libraries facilitated the exponential growth of available documentation. The classification of textual documents is very important since it allows the users to effectively and quickly fly over and understand better the contents of large(More)
Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large sets of documents into a small number of meaningful clusters. Clustering is a very powerful data mining technique for topic discovery from text documents. The partitional clustering algorithms, such as the family of K-means, are(More)
In this paper, the authors study the parameter sensitivity of the technique of particles warm optimization (PSO) for the clustering of data, in particular the text. They experienced the PSO parameters by varying within a range of research and we noted the best result of clustering based on three measures of assessment, internal, which is the index of Davies(More)