IEEE Computational Intelligence Magazine
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Graph mining methods enumerate frequent subgraphs efficiently, but they are not necessarily good features for machine learning due to high correlation among features. Thus it makes sense to perform principal component analysis to reducethe dimensionality and create decorrelated features. We present a novel iterative mining algorithm that captures informative patterns corresponding to major entries of top principal components. It repeatedly callsweighted substructure mining where example weights are updated in each iteration. The Lanczos algorithm, a standard algorithm of eigen decomposition, is employed to update the weights. In experiments, our patterns are shown to approximate the principal components obtained by frequent mining.