Incremental concept-cognitive learning based on attribute topology

Abstract

Incremental learning is an alternative approach for maintaining knowledge by utilizing previous computational results of dynamic data contexts. As a new and important part of incremental learning, incremental Concept-cognitive learning (CCL) is an emerging field of concerning evolution of object or attributes sets and dynamic knowledge processing in the dynamic big data. However, existing incremental CCL algorithms still face some challenges that improve the generalization ability of new concepts, and the previously acquired knowledge should be efficiently utilized to reduce the computational complexity of the algorithm. At the same time, formal concept analysis has become a potential direction of cognitive computing, which can describe the processes of CCL. Attribute topology (AT) as a new representation of formal concepts can clearly display the relationship between new data and original data for reducing the complexity of the CCL process; therefore, we present an incremental concept-cognitive algorithm based on AT for incremental concept calculation, which is expressed by a concept tree. First, a relationship between the new object and some of the original objects is established. Then, on the basis of this finding, we propose an algorithm for updating the concept and presenting them through a concept tree. The algorithm determines the position and subtree of the new object by the relationship between the object and the original objects. Finally, an example is presented to demonstrate that the concept update algorithm based on AT is feasible and effective, and different orders of increments will result in different concept tree structures.