What does a low "Generality" imply?

A low "Generality" score indicates that the clusters formed within the data are more specific and tightly grouped around particular characteristics or patterns. This means that when generality is low, the analysis reveals distinct and precise clusters that encompass fewer variations between the items within those clusters. As a result, the items are closely related to each other—often sharing specific attributes or behaviors—leading to a situation where the clusters are well-defined but may not represent a broad range of items or cases.

Therefore, the concept of having "more and tighter clusters" aligns with the idea that low generality supports the formation of clusters with high levels of similarity among their constituents, thus promoting an in-depth understanding of particular subsets of data rather than a wide overview of a more diverse group. This nuanced understanding is key in analytics when focusing on in-depth analysis of specific groups rather than generalized insights about the entire dataset.