Respuesta :
(a) Yes, there is a difference between the two sets of points. The first set of points is arranged so that they are uniformly spaced, while the second set of points is generated from a uniform distribution over the unit square. This means that the points in the first set will be more regularly spaced, while the points in the second set will be more randomly distributed.
(b) The set of points that will typically have a smaller SSE for K = 10 clusters will be the set of points that are uniformly spaced. This is because when the points are more regularly spaced, it is easier for the clustering algorithm to identify the clusters and to minimize the SSE.
(c) The behavior of DBSCAN on the uniform dataset will likely be different than its behavior on the random dataset. Because the points in the uniform dataset are more regularly spaced, DBSCAN may be able to more easily identify clusters and to assign points to those clusters. In contrast, the points in the random dataset are more randomly distributed, which may make it more difficult for DBSCAN to identify clusters and to assign points to those clusters.
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular clustering algorithm used in data mining and machine learning. It is a density-based algorithm, meaning that it can identify clusters of varying densities (unlike hierarchical clustering, which is based on distance measures and creates clusters with a uniform density). DBSCAN works by identifying points in a dataset that are densely packed together and using them as "core" points to define clusters. It then includes all points that are reachable from the core points within a specified distance (called the "eps" parameter) as part of the cluster. Points that are not part of any cluster are considered "noise" and are typically ignored.
Learn more about DBSCAN, here https://brainly.com/question/29350094
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