Abstract
To automate the process of obtaining knowledge (metadata) about the respondents of sociological surveys or questionnaires, an intelligent information technology has been developed for analyzing weakly-structured multi-dimensional medical-social data. The technology is based on the developed methods of presenting sociological data in the spaces of primary and secondary features and the neural network classification of respondents based on cluster analysis of aggregated sociological data in the space of secondary features. Approbation of the developed technology on real data of sociological surveys showed to increase the reliability of making classification decisions on the respondents’ lifestyle in comparison with the sociologist-analyst and their own definition of respondents.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arsirii E, Sayenko A (2011) Neural network pattern recognition of the public library readers to provide specialized information services. In: Proceedings of the Odessa Polytechnic University, vol 1(35), pp 118–124 (in Russian)
Arsiry E, Zhylenko E (2009) Neural network formation of integrated professional characteristics in the system of distance learning MOODLE. In: Proceedings of the Odessa Polytechnic University, vol 2(32), pp 161–166 (in Russian)
Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in data bases. AI Mag 17(3):37–54. https://doi.org/10.1609/aimag.v17i3.1230
Arsirii O, Babilunha O, Manikaeva O, Rudenko O (2018) Automation of the preparation process weakly-structured multi-dimensional data of sociological surveys in the data mining system. In: Herald of Advanced Information Technology, vol 01(01), pp 9–17 (in Russian). https://doi.org/10.15276/hait.01.2018.1
Rudenko A, Arsirii E (2018) Method of intellectual analysis of weakly structured multidimensional data of sociological surveys. In: Proceedings of the VIIth international conference “Modern Information Technology” (MIT), Odessa, Ukraine, pp 168–169 (in Russian)
Semenov V (2009) Analysis and interpretation of data in sociology. Vladimir State University, Vladimir, Russia (in Russian)
Kislova O (2005) Data mining: opportunities and prospects for application in sociological research. In: Methodology, theory and practice of sociological analysis of modern society, Harkiv, pp 237–243
Chapman P, Clinton J, Kerber R, Khabaza T, Reinartz T, Shearer C, Wirth R (2000) CRISP-DM 1.0 step - by-step data mining guide. SPSS.: Copenhagen, Denmark
Wirth R, Hipp J (2000) CRISP-DM: towards a standard process model for data mining. In: Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, Manchester, UK, pp 29–30
Praveen S, Chandra U (2017) Influence of structured, semi structured, unstructured data on various data models. Int J Sci Eng Res 8(12):67–69
Corbetta P (2011) Social research: theory, methods and techniques. Sage, London. https://doi.org/10.4135/9781849209922
Hunter MG (2002) The repertory grid technique: a method for the study of cognition in information systems. MIS Q 26(1):39–57
Kim DzhO, Myuller ChU (1989) Factor, discriminant and cluster analysis. Finance and Statistics: Moscow, Russia (in Russian)
Hardle W, Simar L (2012) Applied multivariate statistical analysis free preview. Springer, Berlin
Merkert J, Mueller M, Hubl MA (2015) Survey of the application of machine learning in decision support systems. In: Proceedings of the twenty-third European conference on information systems (ECIS 2015), Münster, Germany, pp 1–15
Arsirii E, Manikaeva O, Vasilevskaja O (2015) Development of the decision support subsystem in the systems of neural network pattern recognition by statistical information. Eastern-Euro J Enterp Technol. Math Cybern Appl Aspects, 4(78), 6:4–12
Barsegyan AA, Kupriyanov MS, Stepanenko VV, Holod II (2004) Data analysis methods and modeles: OLAP and data mining. St. Petersburg, Russia, BHV-Peterburg (in Russian)
Sociological survey data “Ukraine - life style” (in Russian). http://edukacjainauka.pl/limesurvey/index.php
Buja A, Swayne DF, Littman ML, Dean N, Hofmann H, Chen L (2008) Data visualization with multidimensional scaling. J Comput Graph Stat 17(2):444–472
Hartigan JA, Wong MA (1979) A k-means clustering algorithm AS 136. Appl Stat 28(1):100–108
Jain A, Murty M, Flynn P (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323
Haykin S (2009) Neural networks and learning machines. McMaster University, Ontario, Canada
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Arsirii, O., Antoshchuk, S., Babilunha, O., Manikaeva, O., Nikolenko, A. (2020). Intellectual Information Technology of Analysis of Weakly-Structured Multi-Dimensional Data of Sociological Research. In: Lytvynenko, V., Babichev, S., Wójcik, W., Vynokurova, O., Vyshemyrskaya, S., Radetskaya, S. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2019. Advances in Intelligent Systems and Computing, vol 1020. Springer, Cham. https://doi.org/10.1007/978-3-030-26474-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-26474-1_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26473-4
Online ISBN: 978-3-030-26474-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)