Abstract
This paper presents an advanced fuzzy C-means (FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced FCM algorithm combines the distance with density and improves the objective function so that the performance of the algorithm can be improved. The experimental results show that the proposed FCM algorithm requires fewer iterations yet provides higher accuracy than the traditional FCM algorithm. The advanced algorithm is applied to the influence of stars’ box-office data, and the classification accuracy of the first class stars achieves 92.625%.
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Wu, S., Pang, Y., Shao, S. et al. Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance. J. Shanghai Jiaotong Univ. (Sci.) 23, 636–642 (2018). https://doi.org/10.1007/s12204-018-1993-y
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DOI: https://doi.org/10.1007/s12204-018-1993-y