Abstract
Most of the association rule mining algorithms use a single seed for initializing a population without paying attention to the effectiveness of an initial population in an evolutionary learning. Recently, researchers show that an initial population has significant effects on producing good solutions over several generations of a genetic algorithm. There are two significant challenges raised by single seed based genetic algorithms for real world applications: (1) solutions of a genetic algorithm are varied, since different seeds generate different initial populations, (2) it is a hard process to define an effective seed for a specific application. To avoid these problems, in this paper we propose a new multiple seeds based genetic algorithm (MSGA) which generates multiple seeds from different domains of a solution space to discover high quality rules from a large data set. This approach introduces m-domain model and m-seeds selection process through which the whole solution space is subdivided into m-number of same size domains and from each domain it selects a seed. By using these seeds, this method generates an effective initial population to perform an evolutionary learning of the fitness value of each rule. As a result, this method obtains strong searching efficiency at the beginning of the evolution and achieves fast convergence along with the evolution. MSGA is tested with different mutation and crossover operators for mining interesting Boolean association rules from different real world data sets and compared the results with different single seeds based genetic algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., Coello, C.A.C.: A survey of multiobjective evolutionary algorithms for data mining: Part I. IEEE Trans. Evol. Comput. 18(1), 4–19 (2014)
Hipp, J., Güntzer, U., Nakhaeizadeh, G.: Algorithms for association rule mining—a general survey and comparison. ACM SIGKDD Explor. Newsl. 2(1), 58–64 (2000)
del Jesus, M.J., Gámez, J.A., González, P., Puerta, J.M.: On the discovery of association rules by means of evolutionary algorithms. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 1(5), 397–415 (2011)
Borgelt, C.: Efficient implementations of apriori and eclat. In: IEEE ICDM Workshop on Frequent Item Set Mining Implementations, pp. 280–296 (2003)
Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)
Qodmanan, H.R., Nasiri, M., Minaei-Bidgoli, B.: Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence. Expert Syst. Appl. 38(1), 288–298 (2011)
Yan, X., Zhang, C., Zhang, S.: Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. Expert Syst. Appl. 36(2), 3066–3076 (2009)
Kabir, M.M.J., Xu, S., Kang, B.H., Zhao, Z.: Comparative analysis of genetic based approach and apriori algorithm for mining maximal frequent item sets. In: IEEE Congress on Evolutionary Computation (CEC), pp. 39–45 (2015)
Martin, D., Rosete, A., Alcala-Fdez, J., Herrera, F.: A new multiobjective evolutionary algorithm for mining a reduced set of interesting positive and negative quantitative association rules. IEEE Trans. Evol. Comput. 18(1), 54–69 (2014)
Kabir, M.M.J., Xu, S., Kang, B.H., Zhao, Z.: A new evolutionary algorithm for extracting a reduced set of interesting association rules. In: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) ICONIP 2015. LNCS, vol. 9490, pp. 133–142. Springer, Heidelberg (2015). doi:10.1007/978-3-319-26535-3_16
Shenoy, P., Srinivasa, K., Venugopal, K., Patnaik, L.: Dynamic association rule mining using genetic algorithms. Intell. Data Anal. 9(5), 439–453 (2005)
Maaranen, H., Miettinen, K., Penttinen, A.: On initial populations of a genetic algorithm for continuous optimization problems. J. Glob. Optim. 37(3), 405–436 (2007)
Maaranen, H., Miettinen, K., Mäkelä, M.M.: Quasi-random initial population for genetic algorithms. Comput. Math. Appl. 47(12), 1885–1895 (2004)
Chang, P.C., Huang, W.H., Ting, C.J.: Dynamic diversity control in genetic algorithm for mining unsearched solution space in TSP problems. Expert Syst. Appl. 37(3), 1863–1878 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Kabir, M.M.J., Xu, S., Kang, B.H., Zhao, Z. (2016). Multiple Seeds Based Evolutionary Algorithm for Mining Boolean Association Rules. In: Cao, H., Li, J., Wang, R. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9794. Springer, Cham. https://doi.org/10.1007/978-3-319-42996-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-42996-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42995-3
Online ISBN: 978-3-319-42996-0
eBook Packages: Computer ScienceComputer Science (R0)