Algorithm for constructing a non-redundant minimax basis of strong associative rules

Associative rules are the type of relations between data that reflect which features or events occur together and how often this happens. Strong associative rules are of interest for those applications where a high degree of confidence of dependencies is required. For example, they are used in information security, computer network analysis and medicine. Excessively large number of identified rules significantly complicates their expert analysis and application. To reduce the severity of this problem, the MClose algorithm is proposed, which extends the capabilities of the well-known algorithm Close. For a given binary context, the proposed algorithm generates non-redundant set of minimax strong associative rules. The algorithm is based on the Galois correspondence and the properties of closed sets.

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