Acta Univ. Agric. Silvic. Mendelianae Brun. 2013, 61(7), 2893-2901 | DOI: 10.11118/actaun201361072893

Knowledge discovery on consumers' behaviour

Pavel Turčínek, Arnoąt Motyčka
Department of Informatics, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic

This paper summarizes results of the research project "Application of modern methods to data processing in the field of marketing research" which was solved at the Department of Informatics, Faculty of Business and Economics of Mendel University in Brno. The most of these results were presented at international conferences.
It describes the use of knowledge discovery techniques on data from marketing research of consumers' behaviour. The paper deals with issues of classification, cluster analysis, correlation and association rules.
For classification there were used various algorithms: multi-layer perceptron neural network, self-organizing (Kohonen's) maps, bayesian networks and generation of a decision tree. Beside Kohonen's maps, which were tested in MATLAB software, all classification methods were tested in Weka software.
In order to find clusters of the methods K-means, Expectation-Maximization, DBSCAN Weka was also used as software for clustering.
Correlation analysis was done based on statistical approach. Generation of association rules was achieved by use of Apriori and the FP-growth algorithm in Weka.
The paper describes above mentioned methods and shows achieved results of exploring data from marketing research on consumers' behaviour.
This article discusses the suitability of these methods usage on such data sets. It also suggests further research possibilities of knowledge discovery on consumers' behaviour.

Keywords: knowledge discovery, classification, cluster analysis, correlation, association rules, consumer behaviour, marketing research
Grants and funding:

This work has been supported by the research design of Mendel University in Brno MSM 6215648904/03.

Received: April 10, 2013; Published: December 24, 2013  Show citation

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Turčínek, P., & Motyčka, A. (2013). Knowledge discovery on consumers' behaviour. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 61(7), 2893-2901. doi: 10.11118/actaun201361072893
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    References

    1. BALOGH, Z., KLIME©, C., 2010: Modelling of education process in LMS using Petri nets structure. In: Proceedings of the IADIS International Conference e-Learning 2010, Part of the IADIS Multi Conference on Computer Science and Information Systems 2010, MCCSIS 2010. Freiburg; Germany: IADIS, 2: 289-291. ISBN 978-972893917-5
    2. BOONE, L. E., KURT, D. L., 2013: Contemporary Marketing. Mason: Cengage Learning, 766 p. ISBN 978-1-111-57971-5.
    3. BRADLEY, N. Marketing Research: Tools and Techniques. New York: Oxford University Press, 531 p. ISBN 0-19-928196-3.
    4. DAŘENA, F., 2008: A research on CRM systems in the Czech Republic. Acta Univ. Agric. et Silvic. Mendel. Brun., 56, 3: 29-34. ISSN 1211-8516. DOI: 10.11118/actaun200856030029 Go to original source...
    5. DAŘENA, F., 2007: Global architecture of marketing information systems. Agricultural Economics, 52, 9: 432-440. ISSN 0139-570X. Go to original source...
    6. DRAPER, N. R., SMITH, H., 1998: Applied Regression Analysis. 3rd ed. New York: Wiley, 706 p. ISBN 978-0-471-17082-2.
    7. ESTER, M., KRIEGEL, H., SANDER, J., XU, X., 1996: A density-based algorithn for discovering clusters in large databases with noise. Proceedings of the 2nd ACM SIGKDD International Conference on Knoelwdge Discovery and Data Mining. Portland: AAAI Press, p. 226-231. on-line: http://dns2.icar.cnr.it/manco/Teaching/2005/datamining/articoli/KDD-96.final.frame.pdf. [cit. 2013-02-21].
    8. EVERITT, B. S., LANDAU, S., LEESE, M., 2001: Cluster Analysis. 4th ed., London: Arnold, 237 p., ISBN 978-0-340-76119-9.
    9. FEJFAR, J., ©«ASTNÝ, J., 2011: Time series clustering in large data sets. Acta Univ. Agric. et Silvic. Mendel. Brun., 59, 2: p. 75-80. ISSN 1211-8516. DOI: 10.11118/actaun201159020075 Go to original source...
    10. FRIEDMAN, N., GEIGER, D., GOLDSZMIDT, M., 1997: Bayesian Network Classifiers. Machine Learning, 29, 2: 131-163. ISSN 0885-6125. DOI: 10.1023/A:1007465528199 Go to original source...
    11. GRABMEIER, J., RUDOLPH, A., 2002: Techniques of Cluster Algorithms in Data Mining. Proceedings of Data Mining and Knowledge Discovery, 6, 4: 303-360. ISSN 1573-756X. DOI: 10.1023/A:1016308404627 Go to original source...
    12. HAN, J., KAMBER, M., 2006: Data Mining: Concepts and Techniques. 2nd ed. San Francisco: Morgen Kaufmann Publishers, 770 p. ISBN 978-1-55860-901-3.
    13. HAN, J., PEI, J., YIN, Y., MAO, R., 2004: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery, 8, 1: 53-87. ISSN 1384-5810. DOI: 10.1023/B:DAMI.0000005258.31418.83 Go to original source...
    14. KAUFMAN, L., ROUSSEEUW, P. J., 2005: Finding Groups in Data: An Introduction to Cluster Analysis. 2nd ed., New Jersey: John Wiley & Sons, 342 p. ISBN 0-471-73578-7.
    15. KNÍ®EK, J., ©INDELÁŘ, J., VOJTĚ©EK, B., BOUCHAL, P., NENUTIL, R., BERÁNEK, L., DĚDÍK, O., 2011: Using markers to aid decision making in diagnostics. International Journal of Tomography and Statistics, 16, W11: 41-55. ISSN 0972-9976.
    16. KOHONEN, T. et al., 2012: SOM PAK: The Self-Organizing Map Program Package. Technical Report A31, Helsinki University of Technology, Laboratory of Computer and Information Science, 1996. on-line: http://www.cis.hut.fi/research/papers/som_tr96.ps. [cit. 2012-10-11].
    17. KOHONEN, T. SCHROEDER, M. R., HUANG, T. S., 2001: Self-Organizing Maps. New York: Springer-Verlag, Inc., 511 p. ISBN 3540679219. Go to original source...
    18. KŘÍ®, J., DOSTÁL, P., 2010: Database System and Soft Computing. Systémová integrace, 17, 4: 17-26. ISSN 1210-9479.
    19. KŘÍ®, J., KLČOVÁ, H., SODOMKA, P., 2011: The Use of Business Intelligence Tools for Prediction and Decisionmaking Processes in the Academic Environment: A Case Study. Innoviation and Knowledge Management - A Global Competitive Advantage, Proceedings of The 16th International Business Information Management Association Conference. IBIMA. Kuala Lumpur, Malaysia: International Business Information Management Association (IBIMA), p. 1259-1265. ISBN 978-0-9821489-5-2.
    20. MELOUN, M., MILITKY, J., 2006: Kompendium statistického zpracování dat: metody a řeąené úlohy. 2nd ed. Praha: Academia, 982 p. ISBN 80-200-1396-2.
    21. MIEHIE, D., SPIGELHALTER, D. J., TAYLOR, C. C., 1994: Machine Learning, Neural and Statistical Classification. New York: Horwood Limited, Ellis, 289 p. ISBN 013106360X
    22. MURTHY, S. K., 1998: Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey. Data Mining and Knowledge Discovery, 2, 4: p. 345-389. ISSN 1384-5810. DOI: 10.1023/A:1009744630224 Go to original source...
    23. POPELKA, O., ©«ASTNÝ, J., 2011: Automatic Generation of Programs. In: Dr. MATTHIAS SCHMIDT (Ed.), Advances in Computer Science and Engineering, ISBN 978-953-307-173-2, InTech, DOI: 10.5772/16012. Available from: http://www.intechopen.com/books/advances-in-computer-science-and-engineering/automatic-generation-of-programs. DOI: 10.5772/16012 Go to original source...
    24. POPELKA, O., ©«ASTNÝ, J., 2007: Generation of mathematic models for enviromental data analysis. Management si Inginerie Economica, 23, 6: 61-66. ISSN 1583-624X.
    25. RÁBOVÁ, I., 2012: Using UML and Petri nets for visualization of business document flow. Acta Univ. Agric. et Silvic. Mendel. Brun., 60, 2: 299-306. ISSN 1211-8516. DOI: 10.11118/actaun201260020299 Go to original source...
    26. ROMESBURG, H. C., 2004: Cluster Analysis For Researchers. North Carolina: Lulu Press, 334 p., ISBN 1-4116-0617-5.
    27. ŘEZANKOVÁ, H., HÚSEK, D., SNÁ©EL, V., 2007: Shluková analýza dat. Praha: Professional Publishing, 196 p. ISBN 978-80-86946-26-9.
    28. SARLE, W. S., 1994: Neural Networks and Statistical Models. Proceedings of the Nineteenth Annual SAS Users Group International Conference, Cary, NC: SAS Institute, p. 1538-1550. on-line: ftp://ftp.sas.com/pub/neural/neural1.ps. [cit. 2013-02-11].
    29. ©KORPIL, V., ©«ASTNÝ, J., 2008: Comparison of Learning Algorithms. 24th Biennial Symposium on Communications. Canada: Kingston, p. 231-234. ISBN 978-1-4244-1945-6. Go to original source...
    30. ©TENCL, M., POPELKA, O., ©«ASTNÝ, J., 2011: Comparison of time series forecasting with artificial neural network and statistical approach. Acta Univ. Agric. et Silvic. Mendel. Brun., 59, 2: 347-352. ISSN 1211-8516. DOI: 10.11118/actaun201159020347 Go to original source...
    31. ©«ASTNÝ, J., TURČÍNEK, P., MOTYČKA, A., 2011: Using Neural Networks for Marketing Research Data Classification. International WSEAS Conference on Mathematical Methods and Techniques in Engeneering & Environmental Science. Catania, Italy: WSEAS Press, 2p. 252-256. ISBN 978-1-61804-046-6.
    32. TURČÍNEK, P., ©«ASTNÝ, J., MOTYČKA, A., 2012a: Usage of Data Mining Techniques on Marketing Research Data. Proceedings of the 11th WSEAS International Conference on Applied Computer and Computational Science (ACACOS '12). Rovaniemi, Finland: WSEAS Press, p. 159-164. ISBN 978-1-61804-084-8.
    33. TURČÍNEK, P., ©«ASTNÝ, J., MOTYČKA, A., 2012b: Usage of cluster analysis in consumer behavior research. Proceedings of the 12th WSEAS International Conference on APPLIED INFORMATICS AND COMMUNICATIONS (AIC '12). Istanbul, Turkey: WSEAS Press, p. 172-177. ISBN 978-1-61804-113-5.
    34. TURČÍNEK, P., ©«ASTNÝ, J., MOTYČKA, A., 2012c: Finding Dependencies in Food Market Consumer Behavior. Proceedings of the European Conference of COMPUTER SCIENCE (ECCS '12). Paris, France: WSEAS Press, p. 67-72. ISBN 978-1-61804-140-1.
    35. TURČÍNKOVÁ, J., KALÁBOVÁ, J., 2011: Preferences of Moravian consumers when buying food. Acta Univ. Agric. et Silvic. Mendel. Brun., 59, 2: 371-376. ISSN 1211-8516. DOI: 10.11118/actaun201159020371 Go to original source...
    36. WEINLICHOVÁ, J., FEJFAR, J., Usage of self-organizing neural networks in evaluation of consumer behaviour. Acta Univ. Agric. et Silvic. Mendel. Brun., 58, 6: p. 625-632. ISSN 1211-8516.
    37. WEKA, 2012: Weka 3 - Data Mining with Open Source Machine Learning Software in Java. [on-line]. HTML Document. 2011. [cit. 2012-10-11]. http://www.cs.waikato.ac.nz/ml/weka/.
    38. ZAKI, M. J.,2000: Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12, 3: 372-390. ISSN 1041-4347. DOI: 10.1109/69.846291 Go to original source...

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