Predictive Graph Mining [chapter]

Andreas Karwath, Luc De Raedt
<span title="">2004</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Graph mining approaches are extremely popular and effective in molecular databases. The vast majority of these approaches first derive interesting, i.e. frequent, patterns and then use these as features to build predictive models. Rather than building these models in a two step indirect way, the SMIREP system introduced in this paper, derives predictive rule models from molecular data directly. SMIREP combines the SMILES and SMARTS representation languages that are popular in computational
more &raquo; ... stry with the IREP rule-learning algorithm by Fürnkranz. Even though SMIREP is focused on SMILES, its principles are also applicable to graph mining problems in other domains. SMIREP is experimentally evaluated on two benchmark databases.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-540-30214-8_1">doi:10.1007/978-3-540-30214-8_1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/i4i4esud35ebtlctfb7yzoazrq">fatcat:i4i4esud35ebtlctfb7yzoazrq</a> </span>
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