In this blog, I am going to describe the DBSCAN clustering algorithm.
DBSCAN operates by defining a neighborhood around each data point and then determining whether the density of points within that neighborhood meets a specified threshold. This threshold, known as the epsilon parameter, determines the maximum distance between points for them to be considered part of the same cluster. Additionally, DBSCAN requires a minimum number of points within a neighborhood to be considered a core point.
By analyzing the density of points, DBSCAN can identify core points, which are surrounded by a sufficient number of neighboring points, as well as border points, which are within the neighborhood of a core point but do not have enough neighboring points to be considered core themselves. On the other hand, isolated points that do not belong to any cluster.
In the context of the project dataset, DBSCAN can effectively identify clusters of states that may have irregular shapes or non-traditional spatial distributions. This is particularly useful when analyzing datasets where the geographical distribution of states does not conform to typical cluster shapes.
Furthermore, DBSCAN can handle missing coordinates in the dataset. Since it focuses on the density of points rather than their exact locations, it can still identify clusters, even if some states have missing latitude and longitude values. This adaptability allows for more comprehensive analysis and insights, even when dealing with incomplete or imperfect geospatial data.
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