## 5 Summary and Conclusions  
The presented method developed to group similar mines into sets using artificial neural networks (i.e., SOM) to optimise business decisions has a real, practical application. The advantage of using SOM in the presented method is that the user does not have to specify a set of indicators relevant to the assumed purpose of grouping objects or binding relations between them. The division into similar groups is made on the basis of criteria found by the artificial neural network itself at the learning stage. This means that it is not possible to provide the criteria that the network used to divide the mining sites into similar groups. On the other hand, it means that it is possible to apply criteria that we are not able to formulate; relatively speaking, they are not known. Such an approach has been successfully used in business decisions concerning the granting of credit, stock market operations, medical diagnoses using the results of diagnostic tests, control of power grids and many other applications. Considering other methods of artificial intelligence, only the use of expert systems provides information on how a system's response is determined. The clustering effect is achieved by the fact that the network, in the process of self-organisation, tries to separate the input data by distinguishing certain classes of similarity among them. This works in such a way that among the input objects (described by the input signals), groups of them are detected (quite automatically) in which signals similar to each other can be placed, which, at the same time, are clearly different from the signals assigned to other groups. Such clustering of data is very useful in many applications, and a number of specialised mathematical techniques have been developed to analyse the data (usually statistically), and to create just such groups of data. The aforementioned techniques, called cluster analysis, have found use, among others, in economics, or in medicine--to investigate which symptoms indicate different variations of the same disease, and which already indicate the presence of a new, possibly unknown disease entity [33]. This led the authors to develop a new method of grouping similar mines into sets, in order to increase management efficiency and their transformation. For example, decisions to merge mines into larger organisations were generally made on the basis of a single criterion--geographical proximity--without considering other factors. It seems reasonable to use the proposed method in this area as well. At the same time, it should be emphasised that the groupings were established on the basis of relevant business indicators. The unit cost of production is the basic indicator in determining a mine's profitability. The validity of the method used and the appropriateness of the groupings can be demonstrated by the median of unit production costs in the individual groups of mining enterprises, which indicates a median cost below the average for the entire population for two groups, and an above average cost for another two. By dividing mines into similar groups, the efficiency of business operations can be increased in real terms by developing optimal management strategies tailored to the specifics of each set of mines, rather than to each individual mine. This reduces the number of such studies in real terms and allows effective measures to be applied more quickly. The divisions obtained also indicate the groups of mines where it is advisable to apply business optimisation measures first. Mines with a unit cost of production higher than the median cost of all analysed mines should be subjected to pro-efficiency measures first. In addition, mines from groups with a unit cost of production below the median set for the entire group of mines can become a benchmark for the others. In a market economy, only those companies that are characterised by the generation of a positive economic result and liquidity can function, last and grow.