Feature selection and dimensionality reduction are important steps in cluster analysis, as they directly impact the quality and interpretability of the clusters.
They help address issues of redundancy, noise, and high dimensionality, which can obscure meaningful patterns and lead to poor clustering performance.
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‘Feature selection’ involves identifying the most relevant variables for clustering.
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Irrelevant or redundant features (variables) can dilute the cluster structure by introducing noise or misleading similarities. In cluster analysis, unsupervised feature selection techniques like variance thresholds or spectral feature selection are often used, as no labels are available to guide the process. Our objective is to retain features that contribute meaningfully to the inherent structure of the data.
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‘Dimensionality reduction’ aims to transform high-dimensional data into a lower-dimensional representation while preserving as much of the relevant structure as possible.
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In cluster analysis, dimensionality reduction helps by reducing computational complexity and minimizing the risk of the "curse of dimensionality," where distances between points become less informative in high dimensions. Popular techniques include Principal Component Analysis (PCA), which projects data onto orthogonal components that explain the maximum variance, and t-SNE or UMAP, which are effective for visualising non-linear relationships in data. We’ll return to these later in the module.
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