Abstract
The crow is one of the most intelligent bird and infamous for observing other birds so that they can steal their food. The crow search algorithm (CSA), a nature-based optimizer, is inspired by the social behavior of crows. Scholars have applied the CSA to obtain efficient solutions to certain function and combinatorial optimization problems. Another popular and powerful method with several real-world applications (e.g., energy, finance, marketing, and medical imaging) is fuzzy clustering. The fuzzy c-means (FCM) algorithm is a critical fuzzy clustering approach given its efficiency and implementation easily. However, the FCM algorithm can be easily trapped in the local optima. To solve this data clustering problem, this study proposes a hybrid fuzzy clustering algorithm combines the CSA and a fireworks algorithm. The algorithm performance is evaluated using eight well-known UCI benchmarks. The experimental analysis concludes that adding the fireworks algorithm improved the CSA’s performance and offered better solutions than those by other meta-heuristic algorithms.
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Liu, H., Zhao, R., Fang, H., Cheng, F., Yun, F., Liu, Y.-Y.: Entropy-based consensus clustering for patient stratification. Bioinformatics 33(17), 2691–2698 (2017)
Nguyen, T.P.Q., Kuo, R.J.: Partition-and-merge based fuzzy genetic clustering algorithm for categorical data. Appl. Soft Comput. 75, 254–264 (2019)
Mistry, K., Zhang, L., Neoh, S.C., Lim, C.P., Fielding, B.: A micro-GA embedded pso feature selection approach to intelligent facial emotion recognition. IEEE Trans. Cybern. 47(6), 1496–1509 (2017)
Perone, C.S., Ballester, P., Barros, R.C., Cohen-Adad, J.: Unsupervised domain adaptation for medical imaging segmentation with self-ensembling. NeuroImage 194, 1–11 (2019)
Liu, H., Li, J., Wu, Y., Fu, Y.: Clustering with outlier removal. IEEE Trans. Knowl. Data Eng. (2019)
Gan, W., Lin, J.C.W., Fournier-Viger, P., Chao, H.C., Philip, S.Y.: HUOPM: high-utility occupancy pattern mining. IEEE Trans. Cybern. 50, 1195–1208 (2019)
Lin, J.C.W., Yang, L., Fournier-Viger, P., Hong, T.P.: Mining of skyline patterns by considering both frequent and utility constraints. Eng. Appl. Artif. Intell. 77, 229–238 (2019)
Zhou, Y., Wang, N., Xiang, W.: Clustering hierarchy protocol in wireless sensor networks using an improved PSO algorithm. IEEE Access 5, 2241–2253 (2017)
Wang, J., Gao, Y., Liu, W., Sangaiah, A.K., Kim, H.J.: An improved routing schema with special clustering using pso algorithm for heterogeneous wireless sensor network. Sensors 19(3), 671 (2019)
Yang, Q., Yang, N., Browning, T.R., Jiang, B., Yao, T.: Clustering product development project organization from the perspective of social network analysis. IEEE Trans. Eng. Manag. (2019)
Chiranjeevi, K., Jena, U., Prasad, P.M.K.: Hybrid cuckoo search based evolutionary vector quantization for image compression. In: Lu, H., Li, Y. (eds.) Artificial Intelligence and Computer Vision. SCI, vol. 672, pp. 89–114. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-46245-5_7
Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: The 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)
Krishnan, M., Yun, S., Jung, Y.M.: Enhanced clustering and ACO-based multiple mobile sinks for efficiency improvement of wireless sensor networks. Comput. Netw. 160, 33–40 (2019)
Zhao, F., Chen, Y., Liu, H., Fan, J.: Alternate PSO-based adaptive interval type-2 intuitionistic fuzzy C-means clustering algorithm for color image segmentation. IEEE Access 7, 64028–64039 (2019)
Wu, Z.X., Huang, K.W., Chen, J.L., Yang, C.S.: A memetic fuzzy whale optimization algorithm for data clustering. In: IEEE Congress on Evolutionary Computation, CEC 2019, Wellington, New Zealand, 10–13 June 2019, pp. 1446–1452. IEEE (2019)
Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010. LNCS, vol. 6145, pp. 355–364. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13495-1_44
Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)
Izakian, H., Abraham, A.: Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Syst. Appl. 38(3), 1835–1838 (2011)
Bezdek, J.C.: Fuzzy mathematics in pattern classification. Ph. D. Dissertation, Applied Mathematics, Cornell University (1973)
Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1–12 (2016)
Li, J., Zheng, S., Tan, Y.: Adaptive fireworks algorithm. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 3214–3221. IEEE (2014)
Acknowledgement
This research was supported by the Ministry of Science and Technology of Taiwan, under grants MOST 108-2221-E-006-111-.
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Wu, ZX., Huang, KW., Yang, CS. (2020). A Fuzzy Crow Search Algorithm for Solving Data Clustering Problem. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_67
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DOI: https://doi.org/10.1007/978-3-030-55789-8_67
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