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This paper concerns the critical decision process of extracting or selectingthe features before applying a clustering algorithm. It is not obvious toevaluate the importance of the features since the most popular methods to do itare usually made for a supervised learning technique process. A clusteringalgorithm is an unsupervised method. It means that there is no known outputlabel to match the input data. This paper proposes a new method to choose thebest dimensionality reduction method (selection or extraction) according to thedata 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-Meansalgorithm along with its metric, the Silhouette Index (SI). This paper presents5 use cases based on a smart city dataset. This research also aims to discussthe impacts, the advantages, and the disadvantages of each choice that can bemade in this unsupervised learning process.

Feature selection or extraction decision process for clustering using PCA and FRSD