Aggregation of Fuzzy Equivalences in Data Exploration by kNN Classifier

The article deals with notions of fuzzy equivalences connectives, dependencies between them, and their applicability to real-life problem as engineering efficient classifiers. It is a fundamental problem in supervised learning - part of artificial intelligence. In the contribution the problem of preservation of their properties in the aggregation process is considered. Moreover, suitability of some fuzzy equivalences and aggregations are proved in the task of data classification by a k-nearest neighbour algorithm.