# A Survey of Some Density Based Clustering Techniques

arXiv: https://arxiv.org/abs/2306.09256 · HTML: https://mlsanity.com/paper/2306.09256/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 13 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Built on by (most cited first, 2 of 2)

- [Fast and explainable clustering in the Manhattan and Tanimoto distance](https://mlsanity.com/paper/2601.08781/) (2026)
- [Scene-Aware Memory Discrimination: Deciding Which Personal Knowledge Stays](https://mlsanity.com/paper/2602.11607/) (2026)

## Abstract (arXiv)

Density Based Clustering are a type of Clustering methods using in data mining for extracting previously unknown patterns from data sets. There are a number of density based clustering methods such as DBSCAN, OPTICS, DENCLUE, VDBSCAN, DVBSCAN, DBCLASD and ST-DBSCAN. In this paper, a study of these methods is done along with their characteristics, advantages and disadvantages and most importantly, their applicability to different types of data sets to mine useful and appropriate patterns.