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

Jean-Sebastien Dessureault; Daniel Massicotte

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arXiv · 2111.10492

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

Jean-Sebastien Dessureault·Daniel Massicotte

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Abstract

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.

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Jean-Sebastien Dessureault, Daniel Massicotte. 2021-11-20. Feature selection or extraction decision process for clustering using PCA and FRSD. https://arxiv.org/abs/2111.10492

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