The phrase "a poor cluster" is correct and usable in written English.
It can be used to describe a group or collection that is lacking in quality or effectiveness, often in contexts like data analysis, networking, or general descriptions of groups.
Example: "The analysis revealed that the data points formed a poor cluster, indicating a lack of correlation among the variables."
Alternatives: "an inadequate group" or "a weak cluster".
The Antlia cluster is a poor cluster very near to our galaxy which has a redshift of 0.0087.
Coma Berenices, located 290 light-years away, is an example of a "poor" cluster, containing only about 40 stars.
This cluster was only known as a poor cluster in the Shapley Supercluster, but when several survey data of different wavelengths are combined, evidence of strong merging is revealed.
Thus, a bad choice of the initial cluster centers can easily converge to a poor cluster assignment.
Suppose we find τ nodes giving a good cluster s for B but a poor cluster for A when the graphs are reordered by column v from X - T. Is this type of substructure likely to arise "by chance"?
The novelty of our approach is that in order to reveal interesting differences between the two types of connectivity data, we look for a set of nodes that form a good cluster with respect to A and a poor cluster with respect to B, or vice versa.
However, the random selection of the cluster head node may obtain a poor clustering setup, and cluster head nodes may be redundant for some rounds of operation.
Notice that Max-Cut and Ratio-Cut do not cluster by intra-cluster similarity and this results in a poor clustering results for image segmentation in comparison to Normalized-Cut [1].
Classical graph-based agglomerative methods employ a variety of similarity measures between nodes to partition PPI networks, but they often result in a poor clustering arrangement that contains one or a few giant core clusters with many tiny ones [1].
So, intuitively, forming AB-1 in (5) should produce a data matrix for which the SVD approach finds good clusters for A and poor clusters for B. Analogously, the opposite holds for BA-1.
The groups can be meaningfully described as a cluster with patients in poor condition on admission (cluster 1, "poor cluster") and a cluster with patients in fair/good condition on admission (cluster 2, "good cluster").
