Design and implementation of incremental algorithm for creation of generalized one-sided concept lattices

In this paper we describe incremental algorithm for generalized one-sided concept lattices based on the Galois connections within Formal Concept Analysis (FCA) framework, which allows to analyse object-attribute models with different structures for truth values of attributes. Therefore, this method provide interesting opportunity for researcher or data analyzer to work with any type of attributes without the need for specific unified preprocessing. The result is that such algorithm can be very useful for any object-attribute models with non-homogenous attributes types, what is quite typical in data mining or online analytical tools. Moreover, it allows to create same FCA-based output in form of concept lattice as in any other case with very precise definition of attribute values and their interpretation. Description of algorithm is extended with practical details regarding its implementation and illustrative example based on the real data from analysis of the secondary school learning process.