Application of Data Mining Methods in the Design and Creation of New Products and Services

The purpose of research is to substantiate the need to use knowledge extraction methods in the design and creation of new products and services and the feasibility of using the Kohonen self-organizing map method through its formation. Such a map helps to identify previously unknown groups, in particular, as in the case of this article – consumer groups, and their analysis will make it possible to form new tariffs for the services of the mobile operator’s billing system. The main reason for the research is to show organizations the ability to design and create innovative products.

Research methods are empirical in nature, based on the collection and accumulation of data on consumer behavior in the market and their subsequent analysis. In order to analyze the collected data, Data Mining methods are used, in particular, the Kohonen self-organizing map method, which allows to obtain automatic clustering of consumers in the market by various characteristics. Clustering was performed using the Kohonen self-organizing map algorithm implemented in the BaseGroup Labs Deductor Studio analytical platform. The choice of this software product is explained by a clear interface and the availability of the required functionality. The study was based on data provided by the mobile operator’s billing system. This is a fairly large amount of data showing the completed operations of mobile operator subscribers.

Results. The article provides an overview of sources that offer possible methods for extracting knowledge and ways to process it. The Kohonen map is also built, which allows you to get information about the current situation for mobile subscribers from various independently selected areas. After analyzing this information, the revealed knowledge is applied in the formation of new tariffs and services of the mobile operator. This method of extracting knowledge can also be applied to other large volumes of data from various fields of activity. However, there is a limitation when using this type of knowledge extraction, which is that the data must be structured. If you use unstructured data, you can consider other methods for extracting knowledge described in this article.

Conclusion. The article considers the stage of knowledge extraction when designing and creating new products and services based on Data Mining methods, in particular the self-organizing Kohonen map. Innovation in the design and creation of products and services is emphasized by the variability of data in accordance with the dynamic behavior of consumers in the market, which causes the need to periodically review the requirements and concepts of products and services brought to the market.

1. Sviridova L.Ye. Practical Application of Data Mining. Alleya nauki = Alley of Science. 2019; 2; 2(29): 917–920. (In Russ.)

2. Isaychenkova V.V., Novikova A.V. Digitalization as a tool to improve the efficiency of business processes. Modern Economy Success = Modern Economy Success. 2019; 3: 141-144. (In Russ.)

3. Suvorov S.V., Tsar’kova N.I., Spiridonova A.K. Analysis of big data of Uber Technologies Inc using Data Mining technology. Upravleniye ekonomicheskimi sistemami: elektronnyy nauchnyy zhurnal = Management of economic systems: electronic scientific journal. 2019; 7(125): 21. (In Russ.)

4. Simonov N.A. Self-organizing Kohonen maps. Matematicheskiye metody upravleniya = Mathematical methods of control. 2019: 100-105. (In Russ.)

5. Bobamuradov O.D. Stages of knowledge extraction from electronic information resources. Tekhnicheskiye nauki. Yevraziyskiy Soyuz Uchenykh (YESU) = Technical sciences. Eurasian Union of Scientists (ESU). 2015; 10(19): 130-133. (In Russ.)

6. Os’kin A.F., Os’kin D.A. Extracting knowledge from databases using a neuro-fuzzy model // Fundamental science. Information technologies. Vestnik polotskogo gosudarstvennogo universiteta. Seriya S = Bulletin of Polotsk State University. Series S. 2018; 12: 9-13. (In Russ.)

7. Amurskiy K.A. Drozhdin V.V. Slesarev Yu.N. The problem of knowledge extraction in information systems. Izvestiya Penzenskogo gosudarstvennogo pedagogicheskogo universiteta im. V.G. Belinskogo = Izvestia of the Penza State Pedagogical University named after V.G. Belinsky. 2010; 22: 96-98. (In Russ.)

8. Lozin I.A., Markelov D.Ye. Extracting knowledge from large data arrays. Izvestiya Samarskogo nauchnogo tsentra Rossiyskoy akademii nauk = Bulletin of the Samara Scientific Center of the Russian Academy of Sciences. 2014; 16; 4(2): 334-339. (In Russ.)

9. Pal’chunov D.Ye. Search and extraction of knowledge: generation of new knowledge based on the analysis of natural language texts. Filosofiya nauki = Philosophy of Science. 2009; 4(43): 70–90. (In Russ.)

10. Gladkov L.A., Shcheglov S.N. Modern trends in the development of knowledge extraction technologies. Izvestiya YUFU. Tekhnicheskiye nauki = Izvestia SFedU. Technical science. 2007; 2: 95–100. (In Russ.)

11. Ignat’yev N.A. Extraction of explicit knowledge from data of different types using neural networks. Vychislitel’nyye tekhnologii = Computational technologies. 2003; 8; 2: 69–73. (In Russ.)

12. Smolyakov A.L. Extracting knowledge from textual information using the template method. Vestnik SPbGU = Bulletin of St. Petersburg State University. 2008; 10(2): 44–50. (In Russ.)

13. Shemenkov P.S. A neural network method of knowledge extraction based on the co-occurrence of key terms. T-Comm - Telekommunikatsii i Transport = T-Comm - Telecommunications and Transport. 2009; 2: 42-43. (In Russ.)

14. Narushev I.R. A neural network based on a self-organizing Kohonen map as a means of detecting anomalous behavior. Okhrana, Bezopasnost’, Svyaz’ = Security, Security, Communication. 2018; 2; 3(3): 194-197. (In Russ.)

15. Salakhutdinov R.Z., Tindova M.G. Application of the Kohonen map for the analysis of real estate prices. Vestnik = Bulletin. 2006; 13(2): 117-119. (In Russ.)

16. Kulikova O.M., Kalugin V.Ye., Pil’nik N.B., Gushchina A.A. Research of innovative development of production in the regions of the Russian Federation using Kohonen maps. Fundamental’nyye issledovaniya = Fundamental research. 2015; 2(25): 5639-5643. (In Russ.)

17. Syabrenko A.P., Tynchenko V.S., Bocharova O.A., Oreshenko T.G. Analysis of the results of certification of employees of enterprises using Kohonen maps. Nauchno-tekhnicheskiy vestnik Povolzh’ya = Scientific and technical bulletin of the Volga region. 2018; 6: 159–162. (In Russ.)

18. Pavlova A.I., Sinel’nikova A.S., Chentayeva Ye.A. Study of self-organizing Kohonen maps. Sovremennyye materialy, tekhnika i tekhnologii = Modern materials, equipment and technologies. 2015; 1(1): 184–187. (In Russ.)

19. Markov V.V. Methodology for extracting and assessing knowledge based on a fuzzy expert model. Izvestiya YUFU. Tekhnicheskiye nauki = Izvestia SFedU. Technical science. 2011; 7(120): 137–141. (In Russ.)

20. Narkevich A.N., Serov A.A., Vinogradov K.A., Narkevich A.A., Shadrin K.V. The use of self-organizing Kohonen maps to analyze the state of Russian regions in terms of socially significant diseases. Sotsial’nyye aspekty zdorov’ya naseleniya = Social aspects of population health. 2016; 4: 9. (In Russ.)