模糊k-模式聚类隶属度值计算
我正在寻找一种模糊聚类分类属性的聚类算法，我发现了 k 模式算法 我已经了解了它的工作方式，但我不明白隶属度或归属矩阵的计算方式是否与模糊 C 均值算法中的该矩阵相同？ 在“否”的情况下，您介意澄清其计算方式吗？
预先感谢您的帮助
I was searching for a clustering algorithm to fuzzy cluster categorical attributes and I found the k-modes algorithm
I've got the way it works but I'm not understanding if the membership or belonging matrix is calculated the same way as this matrix in fuzzy c-means algorithm?
in the "no" case would you mind to clarify the way it's calculated?
thank you in advance for your help
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使用引用论文的符号，问题是
k-Mode 的 W 矩阵是否以与 k-Mean 相同的方式计算？
答案是肯定的，根本不应该有任何差异，当然 W 上的所有约束保持不变;特别是它的转置是随机的 (*)。
k-Mean 和 k-Mode 之间的主要区别在于 Z 的计算，当然还有距离函数。我必须以一种全新的心态重新阅读这篇论文，因为 ATM 对于 k 模式算法（硬算法和模糊算法）来说，关于 Z 的细节对我来说有点模糊（双关语）。
(*) 不那么迂腐地说：对于每个对象，所有 k 个簇的系数 w 之和应该为 1，并且所有这些系数 w 必须为正（因此在 [0,1] 范围内）。
Using the notation of the cited paper, the question is
is the W matrix for k-Mode calculated in the same fashion as for k-Mean ?
The answer is YES, there should be no differences at all, and of course all the constraints on W remain the same; particularly that its transpose be stochastic (*).
The main difference between k-Mean and k-Mode is with regards to the computation of Z, and of course with the distance function. I'd have to re-read the paper with a fresh mind, as ATM the details about Z are a bit fuzzy (pun intended) for me, for both k-Mode algorithms, the hard and the fuzzy one.
(*) Said less pedantically: for each object, the sum of its coefficients w for all k clusters should be 1 and all all these coefficients w must be positive (and hence in the [0,1] range).