have the same description concerning binary features. When applying FCA to the problems we are considering, it is necessary to evaluate the resulting concepts in order to select the most informative of them. This requires measures for evaluating concepts, both external, based on a comparison with a known breakdown into classes, and internal, calculated only from the original data. An interesting feature of FCA is that the result of its application can be represented as a lattice of concepts, which contributes to decision support when identifying subgroups of objects. However, the use of such tools is effective only with a small dimension (a small number of features). Known methods of reduction of the lattice of concepts are aimed at improving the readability and interpretation of the results [1]. For the problems under consideration, a method is needed to form a lattice of small-sized concepts and obtain short descriptions of the concepts themselves. For this, it is proposed to use the ideas of building sparse logical models, including algorithms of test theory and logical data analysis. Logical Data Analysis (LAD) is a data analysis methodology first described by [PERSON] in [2]. LAD creates robust classification models with high accuracy. The main advantage of LAD is that it offers a classification along with an explanation that experts can understand. The first publications were mainly devoted to theoretical and computational development [3]. Subsequently, attention was focused on practical applications. The LAD methodology is widely used in industry. In [4], LAD was used to detect unauthorized aircraft parts. [PERSON] et al. proposed a tool-wear monitoring system based on LAD [5]. The results showed that the developed alarm system detects worn parts and triggers an alarm to replace the cutting tool at an age that is relatively close to the actual failure time. Based on LAD, [PERSON] proposed an approach to automatic diagnostics of bearing failures as well as a multiclass LAD classifier for diagnosing power-transformer failures [6]. In [7], LAD was used to predict the estimated time to equipment failure under various operating conditions. In [8], LAD was applied in the field of occupational health and safety to highlight different types of occupational accidents. In addition, LAD has been applied in aviation to forecast passenger traffic in order to determine overbooking [9]. The proposed method proved to be competitive compared to statistically based tools used by airlines. The multiclass version of LAD has been used for face recognition [10]. The results showed that LAD is superior to other face-recognition methods. LAD has also been successfully used in medicine to diagnose the health status of patients and predict the spread of certain diseases such as breast cancer [11], ovarian cancer [12], coronary disease [13] and ischemic stroke [14]. LAD has found wide application in the financial system to build credit-risk ratings for countries and for financial institutions [15]. LAD has also been applied to several classes of stochastic optimization problems [16]. Mathematical models obtained using the LAD approach can be exact reformulations or internal approximations of problems with random constraints. Formal concept analysis (FCA) was introduced in the early 1980s by [PERSON] as a mathematical theory [17] for the formalization of concepts and conceptual thinking. FCA identifies interesting clusters (formal concepts) in a set of objects and their attributes and organizes them into a structure called a concept lattice. The mathematical basis of FCA is described in [18]. [PERSON] and [PERSON] in [19] reviewed 47 articles on FCA-based software development. The authors classified these documents into 10 categories defined in the ISO 12207 software development standard and visualized them in a concept grid. [PERSON] and [PERSON] in [20] presented an email management system that stores its email in a conceptual grid rather than a conventional tree structure.