Revisiting numerical pattern mining with formal concept analysis
作者： Mehdi Kaytoue , Sergei O. Kuznetsov , Amedeo Napoli
DOI: 10.5591/978-1-57735-516-8/IJCAI11-227
关键词: Formal concept analysis 、 Binary number 、 Scaling 、 Algorithm 、 Contrast (statistics) 、 Mathematics 、 Context (language use) 、 Theoretical computer science 、 Quality (business) 、 Volume (computing)
摘要: We investigate the problem of mining numerical data with Formal Concept Analysis. The usual way is to use a scaling procedure -transforming attributes into binary ones- leading either loss information or efficiency, in particular w.r.t. volume extracted patterns. By contrast, we propose directlywork on more precise and efficient way. For that, notions closed patterns, generators equivalent classes are revisited context. Moreover, two algorithms proposed tested an evaluation involving real-world data, showing quality present approach.
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