# Automated Exploratory Clustering to Democratize Clustering Analysis
## 6 Conclusions  
We can see that larger clustering sets frequently contain the searched clusters. Our method often finds better clusterings than those only selecting one specific clustering. However, finding the good clustering in a set of clusterings is still not trivial. If we set different costs for suboptimal clusterings, we can see that our methods are able to find good clusterings even if the resulting clustering set is small. Our different approaches seem to add value in different use cases for exploratory clustering. The tradeoffs have strikingly different performance when comparing real and synthetic datasets. While they both greatly reduce the sizes of the clustering sets (using four CVIs even more than using two CVIs due to the higher number of clusterings in the first place), they impact the AMI of the \"best\" clustering differently, as follows: while virtually unchanged on real datasets, it is significantly lowered on synthetic data. We can see that this tradeoff of pruning similar clusterings based solely on the similarity --without regard to the Quality function-- can lead to interesting clusterings being pruned. However, due to the different characteristics of the datasets, this only affects the synthetic datasets. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method**Scenario & **CU** & **QC** & **EQC** & **TS** & **CE** \\ \hline skyline (5 CVIs) & **0.292** & 0.306 & 0.320 & 0.334 & 0.348 \\ \hline tradeoff (5 CVIs, \(\epsilon=0.75\)) & 0.338 & 0.298 & 0.258 & 0.218 & 0.178 \\ \hline tradeoff (5 CVIs, \(\epsilon=0.5\)) & 0.385 & 0.315 & 0.245 & 0.175 & 0.104 \\ \hline tradeoff (5 CVIs, \(\epsilon=0.25\)) & 0.464 & 0.362 & 0.260 & 0.158 & 0.056 \\ \hline skyline (2 CVIs) & 0.380 & **0.295** & **0.210** & 0.125 & 0.040 \\ \hline tradeoff (2 CVIs, \(\epsilon=0.75\)) & 0.399 & 0.305 & 0.212 & **0.118** & 0.024 \\ \hline tradeoff (2 CVI, \(\epsilon=0.5\)) & 0.422 & 0.321 & 0.219 & **0.118** & 0.017 \\ \hline tradeoff (2 CVI, \(\epsilon=0.25\)) & 0.445 & 0.338 & 0.230 & 0.122 & 0.014 \\ \hline DBCV & 0.470 & 0.355 & 0.240 & 0.125 & **0.010** \\ \hline SWC & 0.489 & 0.370 & 0.250 & 0.130 & **0.010** \\ \hline autoClues & 0.617 & 0.530 & 0.443 & 0.356 & 0.269 \\ \hline autoCluster & 0.584 & 0.441 & 0.297 & 0.154 & **0.010** \\ \hline Autom14 Clust & 0.562 & 0.424 & 0.286 & 0.148 & **0.010** \\ \hline ml2 dac & 0.578 & 0.436 & 0.294 & 0.152 & **0.010** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average application cost for all scenarios and usages (less is better). Minimum values per scenario are marked in bold. Different Interestingness functions can easily be constructed. However, it is not trivial to select an optimal function for this, as the importance of finding the \"best\" clustering is dependent on the use case, while the user time available is also highly subjective. In some scenarios, a quick glance at a few clusterings might be sufficient, while in others every clustering needs to be investigated deeply. As it is not possible to find the best clustering automatically, it is also not possible to find an always best tradeoff between minimality and coverage, resulting in users decisions again. However, parametrizing an Interestingness function (especially when designed for user-friendliness and using parameters like the size of the retrieved set) might be much more intuitive for an inexperienced user than a clustering algorithm or a complex Automated Clustering system. It could be particularly useful to treat the parameters and the selection of hyperparameters as input of an Interestingness function, to directly optimize this in the AutoML loop, as opposed to the optimization of the Quality function. In this paper, we avoided formally defining what clusterings are, and we also did not detail the generation of any clusterings. While our experiments were performed on crisp clusterings using one particular pipeline, our method is neither limited nor specifically designed for these limitations.