Abstract
Marketing-oriented firms are especially concerned with modeling consumer behavior in order to improve their information and aid their decision processes on markets. For this purpose, marketing experts use complex models and apply statistical methodologies to infer conclusions from data. In the recent years, the application of machine learning has been identified as a promising approach to complement these classical techniques of analysis. In this chapter, we review some of the first approaches that undertake this idea. More specifically, we review the application of Fuzzy-CSar, a machine learning technique that evolves fuzzy association rules online, to a certain consumption problem analyzed. As a differentiating sign of identity from other methods, Fuzzy-CSar does not assume any a prioristic causality (so model) within the variables forming the consumer database. Instead, the system is responsible for extracting the strongest associations among variables, and so, the structure of the problem. Fuzzy-CSar is applied to the real-world marketing problem of modeling web consumers, with the aim of identifying interesting relationships among the variables of the model. In addition, the system is compared with a supervised learning technique, which is able to extract associations between a set of input variables and a pre-fixed output variable, expressly designed for this marketing problem. The results show that Fuzzy-CSar can provide interesting information for marketing experts that was not detected by the classical approach, and that the extraction of fuzzy association rules is an appealing alternative, in general, to refine or complement the modeling results obtained with the use of traditional methods of analysis applied for these purposes; in particular, we focus on, and take as a reference, the structural equation modeling.
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References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Washington D.C., May 1993, pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, Santiago, Chile, September 1994, pp. 487–499 (1994)
Bollen, K.A.: Structural equations with latent variables. Wiley-Interscience, Hoboken (1989) (A division of John Wiley & Sons, Inc.)
Casillas, J., Martínez-López, F.J.: Mining uncertain data with multiobjective genetic fuzzy systems to be applied in consumer behaviour modelling. Expert Systems With Applications 36(2), 1645–1659 (2009)
Csikszentmihalyi, M.: Finding flow: The psychology of engagement with everyday life (1997)
Drejer, A.: Back to basics an beyond: Strategic management – an area where practice and theory are poorly related. Management Decision, 42(3/4), 508–520
Glymour, C., Scheines, R., Spirtes, P., Kelly, K.: Discovering causal structure. Academic Press, Orlando (1987)
Goldberg, D.E.: Genetic algorithms in search, optimization & machine learning, 1st edn. Addison Wesley, Reading (1989)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery 8(1), 53–87 (2004)
Holland, J.H.: Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor (1975)
Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. In: Waterman, D.A., Hayes-Roth, F. (eds.) Pattern-directed inference systems, pp. 313–329. Academic Press, San Diego (1978)
Hong, T.P., Kuo, C.S., Chi, S.C.: A fuzzy data mining algorithm for quantitative values. In: Proceedings International Conference on Knowledge-Based Intelligent Information Engineering Systems, pp. 480–483 (1999)
Hong, T.P., Kuo, C.S., Chi, S.C.: Mining association rules from quantitative data. Intelligent Data Analysis 3, 363–376 (1999)
Hong, T.P., Kuo, C.S., Chi, S.C.: Trade-off between computation time and number of rules for fuzzy mining from quantitative data. International Journal of Uncertainty, Fuzziness, and Knowledge-Based Systems 9(5), 587–604 (2001)
Lenca, P., Meyer, P., Vaillant, B., Lallich, S.: On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid. European Journal of Operational Research 184, 610–626 (2008)
Lent, B., Swami, A.N., Widom, J.: Clustering association rules. In: Procedings of the IEEE International Conference on Data Engineering, pp. 220–231 (1997)
Martínez-López, F.J., Casillas, J.: Marketing intelligent systems for consumer behaviour modelling by a descriptive induction approach based on genetic fuzzy systems. Industrial Marketing Management (2009), doi:10.1016/j.indmarman.2008.02.003
Mata, J., Alvarez, J.L., Riquelme, J.C.: An evolutionary algorithm to discover numeric association rules. In: SAC 2002, pp. 590–594. ACM, New York (2002)
Miller, R.J., Yang, Y.: Association rules over interval data. In: SIGMOD 1997: Proceedings of the 1997 ACM SIGMOD international conference on Management of data, pp. 452–461. ACM, New York (1997)
Novak, T., Hoffman, D., Yung, Y.: Measuring the customer experience in online environments: A structural modelling approach. Marketing Science 19(1), 22–42 (2000)
Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. In: Jagadish, H.V., Mumick, I.S. (eds.) Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Quebec, Canada, pp. 1–12 (1996)
Steenkamp, J., Baumgartner, H.: On the use of structural equation models for marketing modelling. International Journal of Research in Marketing 17, 195–202 (2000)
Stevens, S.S.: On the theory of scales of measurement. Science, 677–680 (1946)
Stevens, S.S.: Measurement, psychophysics and utility. John Wiley, New York (1959)
Wang, K., Tay, S.H.W., Liu, B.: Interestingness-based interval merger for numeric association rules. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, KDD, pp. 121–128. AAAI Press, Menlo Park (1998)
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Orriols-Puig, A., Casillas, J., Martínez-López, F.J. (2010). Automatic Discovery of Potential Causal Structures in Marketing Databases Based on Fuzzy Association Rules. In: Casillas, J., Martínez-López, F.J. (eds) Marketing Intelligent Systems Using Soft Computing. Studies in Fuzziness and Soft Computing, vol 258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15606-9_14
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DOI: https://doi.org/10.1007/978-3-642-15606-9_14
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