What should be considered when selecting inputs for cluster analysis?

When selecting inputs for cluster analysis, it is essential to consider several factors that can significantly influence the quality and interpretability of the results.

Having inputs on similar measurement scales is important because cluster analysis relies on calculating distances between data points. If the variables are on vastly different scales, those with larger ranges can disproportionately affect the clustering outcomes. Therefore, standardizing or normalizing these variables can lead to more meaningful clusters.

Limiting the number of inputs is also a critical consideration. Too many variables can introduce noise into the analysis and make it challenging to discern meaningful patterns. Dimensionality reduction techniques, such as Principal Component Analysis (PCA), may be employed to retain only the most informative variables, ensuring that the analysis remains focused and interpretable.

The measurement level of the inputs should ideally be interval. Interval data provides a meaningful way to measure the distance between points, which is central to many clustering algorithms. While other data types, such as nominal or ordinal, can sometimes be used, they may require additional preprocessing or specialized techniques to be effective in cluster analysis.

Considering all of these aspects—similar measurement scales, a limited number of inputs, and interval measurement levels—ensures a robust and effective clustering process. This comprehensive approach consolidates the input criteria that