Robustness of ML Model in question
Clustering is an unsupervised approach wherein unlike supervised learning its devoid of not knowing the class label or the response or dependent variable. In clustering, the idea is to determine patterns in data such that it can be grouped into cohesive clusters. Such groups will have data points that are similar to each other whilst dissimilar to other data points in other groups. Now, clustering algorithms can broadly be classified into two types, namely, partitional and hierarchical. Some well-known partitional clustering algorithms in the literature are k-means (that works for numerical data only), k-modes (that works for categorical data only), and the k-prototypes (that can work for mixed data (i.e. both numerical and categorical data)).
A fundamental problem associated with partition-based clustering algorithms is not knowing the initial number of groups. And as such researchers using this class of algorithms often fall into this trap by failing to address this fundamental issue. Thus their results are not reproducible.
In literature, there exist several approaches to have proposed to solve this issue of Estimating the possible number of clusters. A well-known approach is the Elbow method proposed by researchers (Kaufman &amp; Rousseeuw, 2009). I fact a group of researchers developed an R package called NbClust that can be used to estimate the number of clusters using the elbow method. This package contains ~30 algorithms for determining the number of clusters.
Recommendation when building a partition-based clustering model
I would recommend the following steps for result reproducibility

setting a random seed value
choose a method to determine the number of clusters
run the experiment at least 5 to 10 times, note the result for each run, and then average the values. Report the averaged value as the final result.

Reference
Kaufman, L., &amp; Rousseeuw, P. J. (2009). Finding groups in data: an introduction to cluster
analysis (Vol. 344): John Wiley &amp; Sons