Computing non-redundant bases of if-then rules from data tables with graded attributes

Abstract

We present a method for computation of non- redundant bases of attribute implications from data tables with fuzzy attributes. Attribute implications are formulas describing particular dependencies of attributes in data. A non-redundant basis is a minimal set of attribute implications such that each attribute implication which is true in a given data (semantically) follows from the basis. Our bases are uniquely given by so-called systems of pseudo-intents. Pseudo-intents are particular granules in data tables. We reduce the problem of computing systems of pseudo-intents to the problem of computing maximal independent sets in certain graphs. We present theoretical foundations, the algorithm, and demonstrating examples.