  This paper concerns the critical decision process of extracting or selecting
the features before applying a clustering algorithm. It is not obvious to
evaluate the importance of the features since the most popular methods to do it
are usually made for a supervised learning technique process. A clustering
algorithm is an unsupervised method. It means that there is no known output
label to match the input data. This paper proposes a new method to choose the
best dimensionality reduction method (selection or extraction) according to the
data scientist's parameters, aiming to apply a clustering process at the end.
It uses Feature Ranking Process Based on Silhouette Decomposition (FRSD)
algorithm, a Principal Component Analysis (PCA) algorithm, and a K-Means
algorithm along with its metric, the Silhouette Index (SI). This paper presents
5 use cases based on a smart city dataset. This research also aims to discuss
the impacts, the advantages, and the disadvantages of each choice that can be
made in this unsupervised learning process.
