Which of the following is important when selecting inputs for cluster analysis?

When selecting inputs for cluster analysis, it is essential that all of the listed considerations are taken into account to ensure effective and meaningful results.

Having inputs with measurement scales that have similar ranges is crucial because cluster analysis relies on distance calculations to determine how data points group together. If the measurement scales differ significantly, variables with larger ranges could disproportionately influence the clustering outcome, leading to misleading results.

Limiting the number of inputs is also important to avoid the "curse of dimensionality." When dealing with too many variables relative to the number of observations, the model can become overly complex, making it harder to identify meaningful patterns in the data. A smaller, well-chosen set of inputs can enhance the interpretability of the clusters formed.

Moreover, using inputs that have an interval measurement level allows for meaningful distance calculations. Interval data provides not only a ranking of values but also equal intervals between them, which is critical for effectively performing clustering since methods like k-means rely on calculating distances between points.

Together, these considerations ensure that the cluster analysis produces robust and interpretable results, making the collective importance of these aspects clear.