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Document clustering is one of the most important research areas of data mining due to its wide application in many fields. To efficiently cope with this problem, a novel document clustering algorithm based on nonnegative matrix factorization (NMF) and support vector data description (SVDD) is proposed in this paper. Experimental results on two well-known document data sets demonstrate the effectiveness...
An XML schema-based similarity algorithm is brought forward. Elements in XML schema are the main and the similarity among elements is the major components of schemas similarity. The algorithm takes full account the structure and semantics of elements. Experimental results reveal the proposed method is very accurately and reliably predicts the similarity. In the mean while, it reduces the complexity...
Document classification has received extensive attention in the past decade due to its wide range applications. To efficiently deal with this problem, a novel document classification algorithm is proposed by using marginal fisher analysis (MFA) and minimax probability machine(MPM). Experimental results on the WebKB data set show that the proposed algorithm achieves much better performance than other...
Document classification has received extensive attention in the past few decades due to its wide applications in many fields. To efficiently deal with this problem, a novel document classification algorithm based on information bottleneck (IB) and least square version of SVM (LS-SVM) is proposed in this paper. Extensive experimental results on the real-word document corpus show that the proposed algorithm...
Classification rule mining is one of the important problems in the field of data mining which aims to extract a small set of rules from the training data set with predetermined targets. In this paper, an efficient classification rule mining algorithm is proposed by using memetic algorithm (MA). Experimental results show that the proposed classification algorithm achieves much better performance than...
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