Fuzzy C-means (FCM) is an important clustering algorithm with broad applications such as retail market data analysis, network monitoring, web usage mining, and stock market prediction. Especially, parameters in FCM have influence on clustering results. However, a lot of FCM algorithm did not solve the problem, that is, how to set parameters. In this study, we present a kind of method for computing parameters values according to role of parameters in the clustering process. New parameters are assigned to membership and typicality so as to modify objective function, on the basis of which Lagrange equation is constructed and iterative equation of membership is acquired, so does the typicality and center equation. At last, a new possibilistic fuzzy C-means based on the weight parameter algorithm (WPFCM) was proposed. In order to test the efficiency of the algorithm, some experiments on different datasets are conducted to compare WPFCM with FCM, possibilistic C-means (PCM), and possibilistic fuzzy C-means (PFCM). Experimental results show that iterative times of WPFCM are less than FCM about 25% and PFCM about 65% on dataset X₁₂. Resubstitution errors of WPFCM are less than FCM about 19% and PCM about 74% and PFCM about 10% on the IRIS dataset.

中文翻译：

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一种权重可能模糊 C 均值聚类算法

模糊 C 均值 (FCM) 是一种重要的聚类算法，具有广泛的应用，例如零售市场数据分析、网络监控、网络使用挖掘和股票市场预测。特别是，FCM 中的参数对聚类结果有影响。但是，很多FCM算法都没有解决问题，即如何设置参数。在这项研究中，我们提出了一种根据参数在聚类过程中的作用来计算参数值的方法。对隶属度和典型性赋予新的参数，对目标函数进行修正，在此基础上构造拉格朗日方程，得到隶属度的迭代方程，进而得到典型性和中心方程。最后，提出了一种新的基于权重参数算法（WPFCM）的可能性模糊C均值。为了测试算法的效率，在不同的数据集上进行了一些实验，以比较 WPFCM 与 FCM、可能性 C 均值 (PCM) 和可能性模糊 C 均值 (PFCM)。实验结果表明，WPFCM 在数据集上的迭代次数小于 FCM 约 25% 和 PFCM 约 65%X ₁₂。在 IRIS 数据集上，WPFCM 的重新代入误差小于 FCM 约 19%、PCM 约 74% 和 PFCM 约 10%。