In this paper, we present a new tree mining algorithm, DRYADEPARENT, based on the hooking principle first introduced in DRYADE. In the experiments, we demonstrate that the branching factor and depth of the frequent patterns to find are key factors of complexity for tree mining algorithms, even if often overlooked in previous work. We show that DRYADEPARENT outperforms the current fastest algorithm, CMTreeMiner, by orders of magnitude on data sets where the frequent tree patterns have a high branching factor.Index Terms-Data mining, mining methods and algorithms, mining tree structured data.
Abstract. Conventional work on scientic discovery such as BACON derives empirical law equations from experimental data. In recent years, SDS introducing mathematical admissibility constraints has been proposed to discover rst principle based law equations, and it has been further extended to discover law equations from passively observed data. Furthermore, SSF has been proposed to discover the structure of a simultaneous equation model representing an objective process through experiments. In this paper, SSF is extended to discover the structure of a simultaneous equation model from passively observed data, and is combined with the extended SDS to discover a quantitative simultaneous equation model reecting the rst principle.
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