Credits Speaker Romeu "@malk_zameth" MOURACompany @linagora License CC-BY-SA 3.0SlideShar j.mp/XXgBAn e ● Mining Graph Data Sources ● Mining the Social Web ● Social Network Analysis for startups ● Social Media Mining and Social Network Analysis ● Graph Mining
"ChunkingLess"Graph Based Induction CL-CBI [Cook et. al.]
Inputs needed1. Minimal frequency where we consider a conformation to be a pattern : threshold2. Number of most frequent pattern we will retain : beam size3. Arbitrary number of times we will iterate: levels
DT-CLGBI(graph: D)begin create_node DT in D if thresold-attained return DT else P <- select_most_discriminative(CL-CBI(D)) (Dy, Dn) <- branch_DT_on_predicate(p) for Di <- Dy DT.branch_yes.add-child(DT-CLGBI(Di)) for Di <- Dn DT.branch_no.add-child(DT-CLGBI(Di))
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