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Mining data has attracted many researchers because of its usefulness of extracting valuable information from the huge volume of continuously increasing databases. In general using labeled data has been more difficult and time consuming than using unlabeled samples. There are several methods that could be used to build a classifier using unlabeled samples. However these may suffer from poor classification quality. In this paper, we propose a semi-supervised approach of classification which uses fewer amount of labeled data and large amount of unlabeled data to build a higher accuracy classifier. Various standard and synthetic dataset have been used to demonstrate the effectiveness of our approach.

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