References
- Agrawal, R. B., K. Deb, and R. Agrawal. 1995. Simulated binary crossover for continuous search space. Complex Systems 9:115–48.
- Brockett, P. L., X. Xia, and R. A. Derrig. 1998. Using Kohonen’s self-organizing feature map to uncover automobile bodily injury claims fraud. The Journal of Risk and Insurance 65:245–74. doi:10.2307/253535.
- Camacho, D. 2015. Bio-inspired clustering: Basic features and future trends in the era of big data. Cybernetics (CYBCONF), 2015 IEEE 2nd International Conference, Poland, 1–6. IEEE.
- Chen, W., G. Xiang, Y. Liu, and K. Wang. 2012. Credit risk evaluation by hybrid data mining technique. Systems Engineering Procedia 3:194–200. doi:10.1016/j.sepro.2011.10.029.
- Cox, E. 1995. A fuzzy system for detecting anomalous behaviors in healthcare provider claims. Intelligent Systems for Finance and Business :111–34.
- Cuevas, E., and M. Cienfuegos. 2014. A new algorithm inspired in the behavior of the social-spider for constrained optimization. Expert Systems with Applications 41:412–25. doi:10.1016/j.eswa.2013.07.067.
- Cuevas, E., M. Cienfuegos, R. Rojas, and A. Padilla. 2015. A computational intelligence optimization algorithm based on the behavior of the social-spider. Computational Intelligence Applications in Modeling and Control, 123–46. Springer.
- Deb, K., K. Sindhya, and T. Okabe. 2007. Self-adaptive simulated binary crossover for real-parameter optimization. Proceedings of the 9th annual conference on Genetic and evolutionary computation, 1187–94. London, UK: ACM.
- Duch, W., K. Grudzinski, and G. Stawski. 2000. Symbolic features in neural networks. Proceedings of the 5th Conference on Neural Networks and Their Applications. Belgrade, Yugoslavia: Citeseer.
- Eggermont, J., J. N. Kok, and W. A. Kosters. 2004. Genetic programming for data classification: Partitioning the search space. Proceedings of the 2004 ACM symposium on Applied computing, 1001–05. Nicosia, Cyprus: ACM.
- Ekin, O., P. L. Hammer, A. Kogan, and P. Winter. 1999. Distance-based classification methods. INFOR: Information Systems and Operational Research 37:337–52.
- Fan, S. K. S., and J. M. Chang. 2009. A parallel particle swarm optimization algorithm for multi-objective optimization problems. Engineering Optimization 41:673–97. doi:10.1080/03052150902752058.
- Huang, Z. 1997. Clustering large data sets with mixed numeric and categorical values. Proceedings of the 1st pacific-asia conference on knowledge discovery and data mining, (PAKDD), 21–34. Hyderabad, India: Citeseer.
- Jie, L., G. Xinbo, and J. Li-Cheng. 2004. A CSA-based clustering algorithm for large data sets with mixed numeric and categorical values. Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on, 2303–07. Hangzhou, China: IEEE.
- Kim, W. 2009. Parallel clustering algorithms: Survey. Parallel Algorithms, Spring.
- Kou, G., Y. Peng, and G. Wang. 2014. Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences 275:1–12. doi:10.1016/j.ins.2014.02.137.
- Luhn, H. P. 1958. A business intelligence system. IBM Journal of Research and Development 2:314–19. doi:10.1147/rd.24.0314.
- Martins, M. C. M., and M. G. Cardoso. 2008. Evaluation of clusters of credit card holders. Open University.
- Nanda, S. J., and G. Panda. 2013. Automatic clustering algorithm based on multi-objective immunized PSO to classify actions of 3d human models. Engineering Applications of Artificial Intelligence 26:1429–41. doi:10.1016/j.engappai.2012.11.008.
- Nanda, S. J., and G. Panda. 2014. A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm and Evolutionary Computation 16:1–18. doi:10.1016/j.swevo.2013.11.003.
- Shukla, U. P., and S. J. Nanda. 2016. Parallel social spider clustering algorithm for high dimensional datasets. Engineering Applications of Artificial Intelligence 56:75–90. doi:10.1016/j.engappai.2016.08.013.
- Talia, D. 2002. Parallelism in knowledge discovery techniques. International Workshop on Applied Parallel Computing, 127–36. Berlin, Heidelberg: Springer.
- Vali, M. 2013. New optimization approach using clustering-based parallel genetic algorithm, arXiv eprint arXiv:1307.5667.
- Williams, G. J., and Z. Huang. 1997. Mining the knowledge mine. Australian Joint Conference on Artificial Intelligence, 340–48. Perth, Australia: Springer.
- Yeo, A. C., K. A. Smith, R. J. Willis, and M. Brooks. 2001. Clustering technique for risk classification and prediction of claim costs in the automobile insurance industry. Intelligent Systems in Accounting, Finance and Management 10:39–50. doi:10.1002/isaf.196.
- Zheng, Z., M. Gong, J. Ma, L. Jiao, and Q. Wu. 2010. Unsupervised evolutionary clustering algorithm for mixed type data. Evolutionary Computation (CEC), 2010 IEEE Congress on, 1–8. Barcelona, Spain:IEEE.