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

• URL: http://arxiv.org/abs/2111.10492v1
• Date: Sat, 20 Nov 2021 01:40:54 GMT
• Title: Feature selection or extraction decision process for clustering using PCA and FRSD
• Authors: Jean-Sebastien Dessureault, Daniel Massicotte
• Abstract summary: This paper proposes a new method to choose the best dimensionality reduction method (selection or extraction) according to the data scientist's parameters. 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)
• Score: 2.6803492658436032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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