How to find degree of fit in K-means++ clustering such that it shows how much percentage the inputs are aligned to each clusters. For instance, input A is in cluster 1 for 0.4 and in cluster 2 for 0.6.
The standard k-means is a hard clustering algorithm, i.e. there is no degree of fit; data points belong to one and only one cluster. Here is a quote from a relevant academic paper (emphasis added):
2.3.1. Hard k-means
[...]
In k-means clustering only bivalent membership degrees of object i to cluster k are allowed: λ[i,k]∈{0,1}. If an object i is a member of a cluster it cannot be a member of any other cluster
K-means++ is just an algorithm for choosing the initial values (seeds) for k-means, and it does not change the fundamental character of k-means as a hard clustering algorithm.