Research Repository

Improving cluster recovery with feature rescaling factors

Amorim, Renato and Makarenkov, Vladimir (2021) 'Improving cluster recovery with feature rescaling factors.' Applied Intelligence, 51 (8). pp. 5759-5774. ISSN 0924-669X

iMWK_ClusteringReScale.pdf - Accepted Version

Download (500kB) | Preview


The data preprocessing stage is crucial in clustering. Features may describe entities using different scales. To rectify this, one usually applies feature normalisation aiming at rescaling features so that none of them overpowers the others in the objective function of the selected clustering algorithm. In this paper, we argue that the rescaling procedure should not treat all features identically. Instead, it should favour the features that are more meaningful for clustering. With this in mind, we introduce a feature rescaling method that takes into account the within-cluster degree of relevance of each feature. Our comprehensive simulation study, carried out on real and synthetic data, with and without noise features, clearly demonstrates that clustering methods that use the proposed data normalization strategy clearly outperform those that use traditional data normalization.

Item Type: Article
Uncontrolled Keywords: Clustering; Feature rescaling; K-Means; Minkowski metric
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 28 Feb 2020 14:30
Last Modified: 23 Sep 2022 19:38

Actions (login required)

View Item View Item