Feature Selection for Unsupervised Problems: The Case of Clustering

Author(s): Kevin Berlemont, PhD

Originally published on Towards AI.

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With the massive growth of data over the last decade, selecting the right feature is becoming a major challenge. A well-known technique in data processing consists of dimensionality reduction. This process tries to remove redundant and irrelevant features that would degrade the performance. These methods can be categorized between feature extraction/construction and feature selection. In the case of feature extraction, the dimensionality of the data is reduced by deriving new features based on the original ones. Examples of this process are Principal Component Analysis [1]… Read the full blog for free on Medium.

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Published via Towards AI