document: Algorithms for storytelling We formulate a new data mining problem called it storytelling as a generalization of redescription mining . In traditional redescription mining , we are given a set of objects and a collection of subsets defined over these objects . The goal is to view the set system as a vocabulary and identify two expressions in this vocabulary that induce the same set of objects . Storytelling , on the other hand , aims to explicitly relate object sets that are disjoint ( and hence , maximally dissimilar ) by finding a chain of ( approximate ) redescriptions between the sets . This problem finds applications in bioinformatics , for instance , where the biologist is trying to relate a set of genes expressed in one experiment to another set , implicated in a different pathway . We outline an efficient storytelling implementation that embeds the CART wheels redescription mining algorithm in an A \* search procedure , using the former to supply next move operators on search branches to the latter . This approach is practical and effective for mining large datasets and , at the same time , exploits the structure of partitions imposed by the given vocabulary . Three application case studies are presented : a study of word overlaps in large English dictionaries , exploring connections between genesets in a bioinformatics dataset , and relating publications in the PubMed index of abstracts .
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