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.
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.