## 1 Introduction  
Granular computing is dedicated to work on data in the form of grouped, similar information vectors. The idea was introduced by [PERSON] [1, 2]. Granulation is an integral part of the fuzzy set theory by the very definition of fuzzy set, where inverse values of fuzzy membership functions are the basic forms of granules. Shortly after [PERSON] proposed the idea of granular computing, the granules were introduced in terms of rough set theory [3] by [PERSON], [PERSON], and [PERSON]. In this theory, granules are defined as classes of indiscernibility relations. Interesting research on more flexible granules based on blocks was conducted by ([PERSON]) (see the LEM2 algorithm), and templates by ([PERSON]), used in classification processes. The granules based on rough inclusions were introduced by ([PERSON] and [PERSON] [4]), based on tolerance or similarity relations, and, more generally, binary relations by ([PERSON] [5], [PERSON] [6; 7; 8]). In the context of rough mereology was proposed by ([PERSON] and [PERSON]), in approximation spaces by ([PERSON] and [PERSON] [9; 10]), and finally in logic for approximate reasoning by ([PERSON], [PERSON] [11], and [PERSON] [12]). Of course, many other authors are conducting considerations on groups of similar objects, which is simply the most natural way of modeling problems; it is impossible to name them all. Let us quote a few very interesting works on various research topics from recent years on granular computing [13; 14; 15; 16; 17; 18]. Additionally, interesting research on the field of granular computation with the use of neural network techniques can be found in the works [19; 20; 21].  
We have developed our methods in terms of granular rough computing paradigm--the internal part of rough sets theory [3]. The computations are based on granules, the groups of objects collected together by fixed similarity measure or metrics. Theoretical background and the framework of discussed methods were proposed by [PERSON] in [22; 23; 24]--the idea of data approximation using rough inclusions. The basic idea was to create the r-indiscernible groups of objects (objects indiscernible in fixed degree) around each training sample, cover the original training decision system using selected granules and create the granular reflection of training data using granules from the covering in the final step. This particular technique is called standard granulation and was proposed in [24]. The initial work was extended later in many variants and contexts--see [25; 26], [PERSON] [27; 28], [PERSON] and [PERSON] [29; 30]. These methods, among others, have found application in classification processes [31], data approximations [30], missing values absorbtion [26; 29], and, in the recent work, these were used as a key component of the new Ensemble model--see [32].  
In the review, we are focusing on decision system size reduction and maintaining the internal knowledge at the same time. Despite the fact that the granulation of the decision systems in a pessimistic case has a square complexity, it is possible to apply classical techniques of transferring methods to big data for the purpose mentioned. In the article, we have described standard granulation [24], concept-dependent [25], layered [25] and homogeneous granulation [33]--designed for symbolic data, and exemplary variants developed for numerical one--with descriptors indiscernibility ratio-epsilon granulation [33; 34].  
The rest of the paper has the following content. In Section 2, there is a detailed description of granulation techniques with toy examples. In Section 3, we present the experimental part for a kNN classifier. In Section 4, we have additional results for the SVM and Naive Bayes classifier. In Section 5, we write about possible future developments of these techniques, and we conclude the paper in Section 6.