  We address the problem of summarizing embedded tree patterns extracted from
large data trees. We do so by defining and mining closed and maximal embedded
unordered tree patterns from a single large data tree. We design an embedded
frequent pattern mining algorithm extended with a local closedness checking
technique. This algorithm is called {\em closedEmbTM-prune} as it eagerly
eliminates non-closed patterns. To mitigate the generation of intermediate
patterns, we devise pattern search space pruning rules to proactively detect
and prune branches in the pattern search space which do not correspond to
closed patterns. The pruning rules are accommodated into the extended embedded
pattern miner to produce a new algorithm, called {\em closedEmbTM-prune}, for
mining all the closed and maximal embedded frequent patterns from large data
trees. Our extensive experiments on synthetic and real large-tree datasets
demonstrate that, on dense datasets, {\em closedEmbTM-prune} not only generates
a complete closed and maximal pattern set which is substantially smaller than
that generated by the embedded pattern miner, but also runs much faster with
negligible overhead on pattern pruning.

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