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Explain disadvantages of density-based clustering in R?
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Answer / Awanish Kumar Jaiswal
Density-based clustering, such as DBSCAN, has several disadvantages. One major drawback is the requirement to set arbitrary values for parameters like minimum number of points (eps) and minimum number of points for a cluster (minPts). These values can significantly impact the results but are not always easy to determine. Another issue is that noise points or outliers can have a large influence on the resulting clusters, especially if they are densely packed. Additionally, density-based clustering may struggle with clusters of varying densities and might not work well for data with a wide range of values.
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