# Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems
## 2 Related Work
### Preprocessing Clustering Techniques  
Regarding research papers using preprocessing clustering techniques, the authors in [16] proposed an evaluation method while using unsupervised clustering algorithms by measuring the usefulness of the task under consideration. For that, they used two example scenarios, among which one included the use of clustering as an automated pre-processing step in a whole data-processing chain. The purpose of this was to improve the overall performance of the system, which can be quantified by some problem-dependent score. The clustering algorithm was just one more \"parameter\" that has to be tuned and this tuning can be achieved in the same way as for all other parameters. What matters is not the evaluation of the quality of the clustering and which meaningful groups it discovers, but the usefulness of the clustering for achieving the final goal.  
The work in [17] presented a study using stochastic gradient Markov Chain Monte Carlo (SG-MCMC) and proposed a subsampling strategy to reduce the variance of applying naive subsampling. For that, the authors partitioned the dataset with the K-Means Clustering algorithm in a preprocessing step and used fixed clustering throughout the entire MCMC simulation. In particular, the clustering procedure was performed on the data samples only once before simulation, and during the sampling procedure, it was easier to compute the new gradient estimator without having extra overhead.  
In [18], the authors presented a study of an unsupervised clustering approach to resolve the frequent data imbalance problem in supervised learning problems in functional genomics. The study proposed preprocessing majority instances by partitioning them into clusters using class purity maximization clustering, which greatly reduced the ambiguity between minority instances and the instances in each cluster. For a moderately or highly imbalanced ratio and low in-class complexity, this technique has a better prediction accuracy than the under sampling method. For an extremely unbalanced ratio, this technique demonstrates an almost perfect recall that reduces the amount of imbalance with significant improvements over previous predictors.  
The work in [19] presents a study on hydrological models wherein the dynamic characteristics, such as seasonal dynamics, are revealed to be a model structural deficiency. The authors proposed a clustering preprocessing framework for the calibration of hydrological models to simulate seasonal dynamic behaviors. Two clustering operations were performed based on the preprocessed climatic index and land-surface index systems. The obtained results show that the performance of the model with a clustering preprocessing framework in the middle and low-flow conditions is significantly improved without reducing the simulation accuracy for high flows.  
From previous research papers, the application of the clustering preprocessing technique is used for subsampling, data imbalance, or model calibration. The specific application to supervised algorithms is still uncommon to the best of our knowledge. This work intends to highlight the importance of these unsupervised techniques. Mainly, the use of clustering for preprocessing supervised algorithms may translate into a boost in their performance.