This relationship suggests that the process scales poorly as the size of the clusters and the number of clusters increase, leading to a significant increase in computation time. The formula $O(bc^2)$ indicates that the total complexity is a function of both the number of clusters (b) and the square of the number of chunks (c) within each cluster.

Given this scaling behavior, it becomes prudent to consider strategies for reducing the computational load. One such strategy could be to limit the number of clusters that are allowed to conflict with one another. By imposing this limitation, unnecessary calculations could be avoided, especially in scenarios where clusters are affected by certain operations but do not end up being combined.

The mention of unusual behavior in relation to Radial Basis Functions (RBFs) suggests that there might be instances where clusters are impacted without leading to a meaningful combination of those clusters. This could indicate a potential area for optimization, as the current approach may result in wasted computational resources. Restricting the impact of RBFs to only relevant clusters could enhance the overall efficiency of the process.