Abstract
In the every kinds of fuzzy clustering algorithm, the fuzzy c-means clustering (FCM) is widely used in the implementation process, because of it has better local search ability and easy operation. But the fuzzy c-means clustering algorithm also has some inherent flaw and the insufficiency. In this paper, we propose some improvement for this shortcomings. Firstly, we improve the calculation method of the membership degree. Secondly, we join the density in the calculation of the membership degree. At the same time, in order to find the global optimal solution, we use the DNA genetic algorithm to assist the FCM algorithm to jump out of local optimal. In this paper, we use the Matlab2014 to realize the simulation and experiment. Firstly, we utilize the test functions and artificial datasets to prove the effectiveness of the improved DNA genetic algorithm. Secondly, we utilize the UCI data sets to validate the effectiveness of the improved fuzzy c-means algorithm. Finally, the improved fuzzy c-means algorithm was used to realize the classification for the Sogou lab corpus of text and the result proved the validity of the algorithm.
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Acknowledgment
The authors would like to express their thanks to the editors and the reviewers for their careful revisions and insightful suggestions. This research is supported by the National Science Foundation of China (Nos. 61876101, 61802234, 61806114, 61472231, 61502283), Social Science Fund Project of Shandong (16BGLJ06, 11CGLJ22), China Postdoctoral Science Foundation Funded Project (2017M612339) and Humanities and social sciences research projects of the Ministry of Education (12YJA630152).
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Jiang, Z., Liu, X. (2019). Fuzzy C-Means Clustering Problem Based on Improved DNA Genetic Algorithm and Point Density Weighting. In: Tang, Y., Zu, Q., Rodríguez García, J. (eds) Human Centered Computing. HCC 2018. Lecture Notes in Computer Science(), vol 11354. Springer, Cham. https://doi.org/10.1007/978-3-030-15127-0_41
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DOI: https://doi.org/10.1007/978-3-030-15127-0_41
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