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Revisiting Numerical Pattern Mining with Formal Concept Analysis

2019-11-12 | |  59 |   32 |   0
Abstract We investigate the problem of mining numerical data with Formal Concept Analysis. The usual way is to use a scaling procedure –transforming numerical attributes into binary ones– leading either to a loss of information or of ef?ciency, in particular w.r.t. the volume of extracted patterns. By contrast, we propose to directly work on numerical data in a more precise and ef?cient way. For that, the notions of closed patterns, generators and equivalent classes are revisited in the numerical context. Moreover, two algorithms are proposed and tested in an evaluation involving real-world data, showing the quality of the present approach.

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