Generalizing Local Density for Density-Based Clustering

Discovering densely-populated regions in a dataset of data points is an essential task for density-based clustering. To do so, it is often necessary to calculate each data point's local density in the dataset. Various definitions for the local density have been proposed in the literature. These definitions can be divided into two categories: Radius-based and k Nearest Neighbors-based. In this study, we find the commonality between these two types of definitions and propose a canonical form for the local density. With the canonical form, the pros and cons of the existing definitions can be better explored, and new definitions for the local density can be derived and investigated.

Automated summary

This study examines different definitions for calculating the local density of data points in density-based clustering, proposing a canonical form to unify these definitions. With the canonical form, the advantages and disadvantages of existing definitions can be better explored, leading to the derivation of new definitions for local density.