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What is the difference between DBSCAN and Kernel Density Estimation

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    $\begingroup$ Can these algortihms actually be compared at all? They try to provide answers to different problems: clustering versus probability density estimation. $\endgroup$
    – cdalitz
    Jun 22, 2020 at 10:20
  • $\begingroup$ Is DBSCAN not estimating density? Though it is used for clustering $\endgroup$ Jun 22, 2020 at 10:25

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DBSCAN algorithm – density-based spatial clustering of applications with noise. This algorithm uses a density-based notion of the cluster and the key idea is that for each point of a cluster the neighborhood of a given radius has to contain at least a minimum number of points.

Kernel Density Estimation

Kernel density estimation is a really useful statistical tool with an intimidating name. Often shortened to KDE, it’s a technique that lets you create a smooth curve given a set of data.

This can be useful if you want to visualize just the “shape” of some data, as a kind of continuous replacement for the discrete histogram. It can also be used to generate points that look like they came from a certain dataset - this behavior can power simple simulations, where simulated objects are modeled off of real data.

Applications of kernel density estimation

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  • $\begingroup$ From your answer, I understood that DBSCAN is used for clustering with some threshold of minpts,radius. whereas KDE is for estimating the distribution of data (shape of data). Is this correct $\endgroup$ Jun 22, 2020 at 5:06
  • $\begingroup$ AFAIK, Yes it is!! $\endgroup$ Jun 22, 2020 at 5:07

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