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The most commonly adopted approach to find valuable information from trees data is to extract frequently occurring subtree patterns from them. Because mining frequent tree patterns has a wide range of applications such as xml mining, web usage mining, bioinformatics, and network multicast routing, many algorithms have been recently proposed to find the patterns. However, existing tree mining algorithms suffer from several serious pitfalls in finding frequent tree patterns from massive tree datasets. Some of the major problems are due to the computationally high cost of the candidate maintenance, the repetitious input dataset scans, and the high memory dependency.
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