Abstract
The data mining task optimization (DMTO) is one of the emerging research areas in the branch of data mining. We discuss the data mining tasks as classification and clustering. The optimization of these tasks is done using soft computing methods. The soft computing is also a new term coined for optimization problems. The classification task is achieved using the support vector machine and results optimized with particle swarm optimization and simplified swarm optimization with exchange local strategy (SSO with ELS). The clustering task is achieved using the kernel fuzzy c-means (KFCM) and results optimized using particle swarm optimization (PSO) and intelligent firefly algorithm (IFA). The results of both the tasks are worked out in the same paper. The results obtained outperform the existing approach SVM with PSO with cuckoo search (CS) and PSO with social spider optimization (SSO) for classification, KFCM with PSO with Bacteria Foraging Optimization (BFO) for clustering in data mining task optimization framework.
Please note that the LNCS Editorial assumes that all authors have used the western naming convention, with given names preceding surnames. This determines the structure of the names in the running heads and the author index.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fayyad, U., G. Piatesky-Shapiro, and P. Smyth. 1996. From Data Mining to Knowledge Discovery in Databases. AI Magzine 17 (3): 37–54.
Beni, G., and J. Wang. 1989. Cellular Robotic Systems. http://en.wikipedia.org/wiki/Swarm_intelligence.
Graves, D., and W. Pedrycz. 2010. Kernel-Based Fuzzy Clustering and Fuzzy Clustering: A Comparative Experimental Study. In Fuzzy Sets and Systems, 522–543.
Bae, Changseok, Wei-Chang Yeh, Noorhaniza Wahid, Yuk Ying Chung, and Yao Liu. 2012. A New Simplified Swarm Optimization (SSO) Using Exchange Local Search Scheme. International Journal of Innovative computing, Information and Control (IJICIC) 8 (6): 4391–4406.
Liu, Xiaoyong, and Hui Fu. 2014. PSO-Based Support Vector Machine with Cuckoo Search Technique for Clinical Disease Diagnoses, 1–7. Hindawi Publishing Corporation.
Yang, Xin-She, and Suash Deb. 2009. Cuckoo Search via Levy Flights. IEEE, 210–214.
James, J.Q., and O. Victor. 2015. A Social Spider Algorithm for Global Optimization. Applied Soft Computing 30: 614–627.
Liu, Yao, Yuk Ying Chung, and Wei Chang Yeh. 2012. Simplified Swarm Optimization with Sorted Local Search for Golf Data Classification. In IEEE World Congress on Computational Intelligence.
Marr, J. 2003. Comparison of Several Clustering Algorithms for Data Rate Compression of LPC Parameters. In IEEE International Conference on Acoustics Speech, and Signal Processing, vol. 6, 964–966.
Wong, K.C., and G.C.L. Li. 2008. Simultaneous Pattern and Data Clustering for Pattern Cluster Analysis. IEEE Transaction on Knowledge and Data Engineering 20: 911–923 (Los Angeles, USA).
Kora, P., and S.R. Kalva. 2015. Hybrid Bacterial Foraging and Particle Swarm Optimization for detecting Bundle Branch Block, SpringerPlus, vol. 4, no. 1.
Korani, W.M. 2008. Bacterial Foraging Oriented by Particle Swarm Optimization Strategy for PID Tuning. In Proceedings of the 2008 GECCO Conference Companion on Genetic and Evolutionary Computation—GECCO ’08.
Fateen, S.E.K., and A. Bonilla-Petriciolet. 2013. Intelligent Firefly Algorithm for Global Optimization. In Studies in Computational Intelligence, 315–330.
Yang, X.-S. 2014. Firefly Algorithms, Nature-Inspired Optimization Algorithms, 111–127.
UCI Machine Learning Repository. http://archive.ics.uci.edu/ml.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gagnani, L., Wandra, K. (2020). Data Mining Task Optimization with Soft Computing Approach. In: Raju, K., Govardhan, A., Rani, B., Sridevi, R., Murty, M. (eds) Proceedings of the Third International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-15-1480-7_48
Download citation
DOI: https://doi.org/10.1007/978-981-15-1480-7_48
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1479-1
Online ISBN: 978-981-15-1480-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)