Abstract
Unitary clustering algorithm, not well adapted for fuzzy medical data sets, may result in low clustering accuracy and other problems. This paper investigates and compares the effects of various clustering methods to achieve improvements. First, unitary clustering algorithms such as k-means, FANNY, FCM, and etc. are achieved, then FCM algorithm was improved into CFCM algorithm, which increases the accuracy to a certain extent. Second, on this basis, in order to better adapt to the diversity of characteristics of fuzzy medical data, weighted co-association matrix is adopted to achieve integration, and consistency function is designed to present a fuzzy clustering ensemble algorithm. Finally, experiments shows that the Fuzzy Clustering Ensemble Algorithm can solve the problem of low accuracy in unitary clustering algorithm with higher stability, accuracy and robustness.
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Acknowledgements
1. Major Scientific Research Project in Higher School in Hebei Province (Grant No. ZD201310-85), 2. Funding Project of Science & Technology Research and Development in Hebei North University (Grant No. ZD201301), 3. Major Projects in Hebei Food and Drug Administration (Grant No. ZD2015017), 4. Major Funding Project in Hebei Health Department (Grant No. ZL20140127), 5. Youth Funding Science and Technology Projects in Hebei Higher School (Grant No. QN2016192), with Hebei Province Population Health Information Engineering Technology Research Center.
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Zhao, Z., Liu, Y., Li, J., Wang, J., Wang, X. (2018). A Study of Fuzzy Clustering Ensemble Algorithm Focusing on Medical Data Analysis. In: Yen, N., Hung, J. (eds) Frontier Computing. FC 2016. Lecture Notes in Electrical Engineering, vol 422. Springer, Singapore. https://doi.org/10.1007/978-981-10-3187-8_37
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DOI: https://doi.org/10.1007/978-981-10-3187-8_37
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