Accelerating concept-dependent granulation technique using data decomposition

The granular computing approach as a paradigm in approximate reasoning deals with the processing of knowledge into granules that consist of entities similar in information content. Within the framework of rough set theory, proposed 40 years ago by Zdzislaw Pawlak and developed since then by many authors, granulation is an important area of research. Granulation techniques have found application in many areas of data mining in classification, feature selection, clustering and approximation processes for decision-making systems, among others. The current work is located in the development of approximation techniques for decision systems using rough inclusions proposed by Polkowski. Polkowski proposed the hypothesis that granules induced in a data set of a universe of objects should lead to new object representing them, and such granulated counterparts should preserve the information content of the data. This hypothesis has been verified in a number of research papers by Polkowski and Artiemjew. It has been proven that one of the best granulation techniques from the proposed family of methods is concept-dependent granulation. In which, on selected data, the degree of approximation of decision-making systems reaches more than 90 percent reduction in the size of training systems while maintaining the classification efficiency of the original training data. The undoubted disadvantage of this technique is the quadratic computational complexity - which forces us to look for a way to apply it on big data sets.The current work is one in a series of papers on the application of techniques used to deal with big data sets in the context of accelerating the concept-dependent granulation method. In this paper, we test whether granulation of training data divided into subgroups with post-granulation object fusion is competitive - in terms of running time and classification efficiency - to using a full training system in the granulation and classification process. The posed experimental problem is verified on a selected decision-making systems from UCI repository and using simple kNN classifier.