Abstract
There exist several fitness function proposals based on a combination of weighted objectives to optimize the discovery of association rules. Nevertheless, some differences in the measures used to assess the quality of association rules could be obtained according to the values of such weights. Therefore, in such proposals it is very important the user’s decision in order to specify the weights or coefficients of the optimized objectives. Thus, this work presents an analysis on the sensitivity of several quality measures when the weights included in the fitness function of the existing QARGA algorithm are modified. Finally, a comparative analysis of the results obtained according to the weights setup is provided.
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
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the International Conference on Very Large Databases, pp. 478–499 (1994)
Alatas, B., Akin, E.: An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules. Soft Computing 10(3), 230–237 (2006)
Alatas, B., Akin, E., Karci, A.: MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules. Applied Soft Computing 8(1), 646–656 (2008)
Alcalá-Fdez, J., Sánchez, L., García, S., del Jesus, M.J., Ventura, S., Garrell, J.M., Otero, J., Romero, C., Bacardit, J., Rivas, V.M., Fernández, J.C., Herrera, F.: Keel: A software tool to assess evolutionary algorithms for data mining problems. Soft Computing 13(3), 307–318 (2009)
Li, D., Deogun, J., Spaulding, W., Shuart, B.: Towards missing data imputation: A study of fuzzy K-means clustering method. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 573–579. Springer, Heidelberg (2004)
Guvenir, H.A., Uysal, I.: Bilkent university function approximation repository (2000), http://funapp.cs.bilkent.edu.tr
Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Empirical analysis of using weighted sum fitness functions in NSGA-II for many-objective 0/1 knapsack problems. In: Proceedings of the International Conference on Computer Modelling and Simulation, pp. 71–76 (2009)
Martínez-Ballesteros, M., Martínez-Álvarez, F., Troncoso, A., Riquelme, J.C.: An evolutionary algorithm to discover quantitative association rules in multidimensional time series. Soft Computing 15(10), 2065–2084 (2011)
Martínez-Ballesteros, M., Martínez-Álvarez, F., Troncoso, A., Riquelme, J.C.: Selecting the best measures to discover quantitative association rules. Neurocomputing (in press, 2013), doi: http://dx.doi.org/10.1016/j.neucom.2013.01.056
Martínez-Ballesteros, M., Riquelme, J.C.: Analysis of measures of quantitative association rules. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds.) HAIS 2011, Part II. LNCS, vol. 6679, pp. 319–326. Springer, Heidelberg (2011)
Mata, J., Álvarez, J., Riquelme, J.C.: Mining numeric association rules with genetic algorithms. In: Proceedings of the International Conference on Adaptive and Natural Computing Algorithms, pp. 264–267 (2001)
Pachón Álvarez, V., Mata Vázquez, J.: An evolutionary algorithm to discover quantitative association rules from huge databases without the need for an a priori discretization. Expert Systems with Applications 39(1), 585–593 (2012)
Pears, R., Koh, Y.S., Dobbie, G., Yeap, W.: Weighted association rule mining via a graph based connectivity model. Information Sciences 218, 61–84 (2013)
Soto, W., Olaya-Benavides, A.: A genetic algorithm for discovery of association rules. In: Proceedings of the International Conference of the Chilean Computer Science Society, pp. 289–293 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martínez-Ballesteros, M., Martínez-Álvarez, F., Troncoso, A., Riquelme, J.C. (2013). A Sensitivity Analysis for Quality Measures of Quantitative Association Rules. In: Pan, JS., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2013. Lecture Notes in Computer Science(), vol 8073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40846-5_58
Download citation
DOI: https://doi.org/10.1007/978-3-642-40846-5_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40845-8
Online ISBN: 978-3-642-40846-5
eBook Packages: Computer ScienceComputer Science (R0)