Large-Scale Arabic Text Classification - Abu Tair, Mohammed M.
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Text classification has become one of the most important techniques in text mining. A number of machine learning algorithms have been introduced to deal with automatic text classification. One of the common classification algorithms is the k-NN algorithm which is known to be one of the best classifiers applied for different languages including Arabic language. However, the k-NN algorithm is of low efficiency because it requires a large amount of computational power. Such a drawback makes it unsuitable to handle a large volume of text documents with high dimensionality and in particular in the…mehr

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Produktbeschreibung
Text classification has become one of the most important techniques in text mining. A number of machine learning algorithms have been introduced to deal with automatic text classification. One of the common classification algorithms is the k-NN algorithm which is known to be one of the best classifiers applied for different languages including Arabic language. However, the k-NN algorithm is of low efficiency because it requires a large amount of computational power. Such a drawback makes it unsuitable to handle a large volume of text documents with high dimensionality and in particular in the Arabic language. This book, therefore, introduces a high performance parallel classifier for large-scale Arabic text that achieves the enhanced level of efficiency, scalability, and accuracy. The parallel classifier based on the sequential k-NN algorithm. We tested the classifier using the OSAC corpus. We studied the performance of the parallel classifier on a multicomputer cluster. The results indicate that the parallel classifier has very good speedups and scalability and is capable of handling large document collections with higher classification results.
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Autorenporträt
Programmer, The College of Science and Technlogy, KhanYounis - Palestine, MSc & BSc in Information Technology from The Islamic University of Gaza - Palestine. Research Interest: Data Mining, Text Mining, Distributed Data Mining, Software Development, Web Computing, and Computer Networks.