Mining frequent patterns in attributed trees (that we call asubtrees), which combines tree mining and itemset mining, requires the exploration of a huge search space. To make our approach scalable, we investigate the mining of condensed representations. For attributed trees, the classical concept of closure involves both itemset closure and structural closure. IMIT includes the implementation of three algorithms for mining all patterns, closed patterns w.r.t. itemsets (content) and/or structure in attributed trees 1 2 3. We show that, for low support values, mining content-closed attributed trees is a good compromise between non-redundancy of solutions and execution time.
- Frequent Pattern Mining in Attributed Trees. 17th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD'13), J. Pei et al. (Eds.): PAKDD 2013, Part I, LNAI 7818, Pp. 26–37. Springer, Heidelberg (2013).(2013). ↩︎
- Extraction de motifs fréquents dans des arbres attribués. 13ème Conférence Francophone sur l’Extraction et la Gestion des Connaissances (EGC'13). Revue des Nouvelles Technologies de l’Information, volume E-24.(2013). ↩︎
- Frequent Pattern Mining in Attributed Trees: Algorithms and Applications. Knowledge and Information Systems.(2016). ↩︎