Abstract
Association Rules (ARs) are the most important tool of Data Mining (DM) used to extract useful information stored in large databases during the last years. Motivated by the success of population-based metaheuristics dealing with this amount of data, we propose to develop a faster approach of the Bat algorithm based on ARM. Our approach is evaluated on a real database of population with or without diabetes. The proposed algorithm has better optimization accuracy and time complexity compared with the old version of the algorithm.
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
Similar content being viewed by others
References
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Record, vol. 22, pp. 207–216. ACM (1993)
Amraoui, H., Mhamdi, F., Elloumi, M.: Fast exhaustive search algorithm for discovering relevant association rules
Amraoui, H., Mhamdi, F., Elloumi, M.: Survey of metaheuristics and statistical methods for multifactorial diseases analyses. AIMS Med. Sci. 4, 291–331 (2017)
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Djenouri, Y., Belhadi, A., Belkebir, R.: Bees swarm optimization guided by data mining techniques for document information retrieval. Expert Syst. Appl. 94, 126–136 (2018)
Djenouri, Y., Djenouri, D., Belhadi, A., Fournier-Viger, P., Lin, J.C.W., Bendjoudi, A.: Exploiting GPU parallelism in improving bees swarm optimization for mining big transactional databases. Inf. Sci. 496, 326–342 (2018)
Heraguemi, K.E., Kamel, N., Drias, H.: Association rule mining based on bat algorithm. J. Comput. Theor. Nanosci. 12(7), 1195–1200 (2015)
Jayabarathi, T., Raghunathan, T., Gandomi, A.H.: The bat algorithm, variants and some practical engineering applications: a review. In: Yang, X.-S. (ed.) Nature-Inspired Algorithms and Applied Optimization. SCI, vol. 744, pp. 313–330. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67669-2_14
Li, Y., Li, Y.: E-commerce order batching algorithm based on association rule mining in the era of big data. In: 2018 Chinese Control And Decision Conference (CCDC), pp. 1934–1939. IEEE (2018)
Neelima, S., Satyanarayana, N., Krishna Murthy, P.: Minimizing frequent itemsets using hybrid ABCBAT algorithm. In: Satapathy, S.C., Bhateja, V., Raju, K.S., Janakiramaiah, B. (eds.) Data Engineering and Intelligent Computing. AISC, vol. 542, pp. 91–97. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-3223-3_9
Smith, J.W., Everhart, J., Dickson, W., Knowler, W., Johannes, R.: Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In: Proceedings of the Annual Symposium on Computer Application in Medical Care, p. 261. American Medical Informatics Association (1988)
Talebi, B., Dehkordi, M.N.: Sensitive association rules hiding using electromagnetic field optimization algorithm. Expert Syst. Appl. 114, 155–172 (2018)
Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12538-6_6
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Amraoui, H., Mhamdi, F., Elloumi, M. (2019). Fast Bat Algorithm for Predicting Diabetes Mellitus Using Association Rule Mining. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-35231-8_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35230-1
Online ISBN: 978-3-030-35231-8
eBook Packages: Computer ScienceComputer Science (R0)