Web page classification is one of the essential techniques for Web mining. This paper proposes a binary hierarchical
classifier for multi-class support vector machines for web page classification. This method applies truncated singular
value decomposition on the training data that reduces its dimension and the noise data. After the truncated singular value
decomposition on the training data, it uses the improved k-means algorithm design the binary hierarchical structure, the
improved k-means algorithm makes the separability of one macro-class is the smallest, makes the separability of two
macro-classes is the largest. The result of experiment performed on the training datasets shows that this algorithm can
enhance precision of web page classification.
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